(AMARAVATI, AMRITAPURI, BANGALORE, COIMBATORE, CHENNAI)

B.TECH COMPUTER SCIENCE AND ENGINEERING

(ARTIFICIAL INTELLIGENCE) (BTC-AIE)

CURRICULUM 2022

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 GENERAL INFORMATION

ABBREVIATIONS USED IN THE CURRICULUM

Cat - Category
L - Lecture
T - Tutorial
P - Practical
Cr - Credits
ENGG - Engineering Sciences (including General, Core and Electives)
HUM - Humanities (including Languages and others)
SCI - Basic Sciences (including Mathematics)
PRJ - Project Work (including Seminars)

AES - Aerospace Engineering

AIE - Computer Science and Engineering - Artificial Intelligence
BIO - Biology
CCE - Computer and Communication Engineering
CHE - Chemical Engineering
CHY - Chemistry
CSE - Computer Science and Engineering
CVL - Civil Engineering
CUL - Cultural Education
EAC - Electronics and Computer Engineering
ECE - Electronics and Communication Engineering
EEE - Electrical and Electronics Engineering
ELC - Electrical and Computer Engineering
HUM - Humanities
MAT - Mathematics
MEE - Mechanical Engineering
PHY - Physics

Course Outcome (CO) – Statements that describe what students are expected to know, and are able to do at the
end of each course. These relate to the skills, knowledge and behaviour that students acquire in their progress
through the course.

Program Outcomes (POs) – Program Outcomes are statements that describe what students are expected to know
and be able to do upon graduating from the Program. These relate to the skills, knowledge, attitude and behaviour
that students acquire through the program. NBA has defined the Program Outcomes for each discipline.

PROGRAM OUTCOMES FOR ENGINEERING

1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an
engineering specialization to the solution of complex engineering problems.
2. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems
reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering
sciences.
3. Design/development of solutions: Design solutions for complex engineering problems and design system
components or processes that meet the specified needs with appropriate consideration for the public health
and safety, and the cultural, societal, and environmental considerations.
4. Conduct investigations of complex problems: Use research-based knowledge and research methods
including design of experiments, analysis and interpretation of data, and synthesis of the information to
provide valid conclusions.
5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and
IT tools including prediction and modelling to complex engineering activities with an understanding of the
limitations.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health,
safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering
practice.
7. Environment and sustainability: Understand the impact of the professional engineering solutions in societal
and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the
engineering practice.
9. Individual and team work: Function effectively as an individual, and as a member or leader in diverse
teams, and in multidisciplinary settings.
10. Communication: Communicate effectively on complex engineering activities with the engineering
community and with society at large, such as, being able to comprehend and write effective reports and design
documentation, make effective presentations, and give and receive clear instructions.
11. Project management and finance: Demonstrate knowledge and understanding of the engineering and
management principles and apply these to one’s own work, as a member and leader in a team, to manage
projects and in multidisciplinary environments.
12. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent
and life-long learning in the broadest context of technological change.

PROGRAM SPECIFIC OUTCOMES FOR CSE-AI

After completing the B.Tech CSE-AI program, the students will,

1. PSO1: Have the ability to apply mathematical and analytical techniques to model complex problems.
2. PSO2: Have a strong foundation in programming, together with knowledge of modern languages, tools
and technologies needed to build secure, robust software systems.
3. PSO3: Have the knowledge of AI and ML techniques required for the design and development of
intelligent systems to solve real world problems.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

SEMESTER I

Cat. Code Title LTP Credit

22MAT110
SCI Mathematics for Computing 1 213 4
22PHY106
SCI Computational Physics 203 3
22AIE101
ENGG Problem Solving & C Programming 213 4
22AIE102
ENGG Elements of Computing Systems 1 203 3
22MAT121
ENGG Discrete Mathematics 203 3
22ADM101 Foundations of Indian Heritage
HUM 200 2

HUM 19ENG111 Technical Communication 203 3

HUM 22AVP103 Mastery Over Mind (MAOM) 102 2

TOTAL 37 24

SEMESTER II

Cat. Code Title LTP Credit

SCI 22MAT122 Mathematics for Computing 2 213 4

ENGG 22AIE111 Object Oriented Programming in Java 213 4

ENGG 22AIE112 Data Structures & Algorithms 1 213 4
ENGG 22AIE113 Elements of Computing Systems - 2 203 3
22AIE114 Introduction to Electrical and Electronics
ENGG 203 3
Engineering
ENGG 22AIE115 User Interface Design 203 3

HUM 22ADM111 Glimpses of Glorious India 200 2

TOTAL 35 23

SEMESTER III
Cat Code Title LTP Cr
SCI 22MAT220 Mathematics for Computing 3 213 4
ENGG 22AIE201 Fundamentals of AI 203 3
ENGG 22AIE202 Operating Systems 203 3
ENGG 22AIE203 Data Structures & Algorithms 2 203 3
ENGG 22AIE204 Introduction to Computer Networks 203 3
ENGG 22AIE205 Introduction to Python 103 2
ENGG 22BIO201 Intelligence of Biological Systems - 1 200 2
ENGG Free Elective 1** 200 2
HUM Amrita Values Program 100 1
Total 35 23

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

SEMESTER IV
Cat Code Title LTP Cr
SCI 22MAT230 Mathematics for Computing 4 213 4
ENGG 22AIE211 Introduction to Communication & IoT 203 3
ENGG 22AIE212 Design and Analysis of Algorithms 203 3
ENGG 22AIE213 Machine Learning 203 3
ENGG 22AIE214 Introduction to AI Robotics 203 3
ENGG 22BIO211 Intelligence of Biological Systems 2 203 3
HUM Amrita Values Program 100 1
HUM 19ENV300 Environmental Science P/F
HUM 19SSK211 Soft Skills I 103 2
Total 35 22

SEMESTER V

Cat Code Title LTP Cr

ENGG 22AIE301 Probabilistic Reasoning 203 3
ENGG 22AIE302 Formal language and Automata 210 3
ENGG 22AIE303 Database Management Systems 213 4
ENGG 22AIE304 Deep Learning 203 3
ENGG 22AIE305 Introduction to Cloud Computing 203 3
*
ENGG Professional Elective 1 203 3
HUM 19SSK301 Soft Skills II 103 2
HUM 19LIV390 Live-in-Labs I [3]

Total 32 21 + [3]

SEMESTER VI

Cat Code Title LTP Cr

ENGG 22AIE311 Software Engineering (Project-Based) 203 3
ENGG 22AIE312 Big Data Analytics 203 3
ENGG 22AIE313 Computer Vision & Image Processing 213 4
ENGG 22AIE314 Computer Security 203 3
ENGG 22AIE315 Natural Language Processing 203 3
*
ENGG Professional Elective 2 203 3
Soft Skills III
HUM 19SSK311 103 2

ENGG 19LIV490 Live-in-Labs II [3]

Total 34 21 +[3]

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

SEMESTER VII

Cat Code Title LTP Cr

ENGG 22AIE401 Reinforcement Learning 203 3

ENGG Professional Elective 3* 203 3

ENGG Professional Elective 4* 203 3
HUM 19LAW300 Indian Constitution 100 P/F
ENGG Free Elective 2** 300 3
PRJ 22AIE498 Project Phase 1 6
Total 19 18

SEMESTER VIII

Cat Code Title LTP Cr

PRJ 22AIE499 Project Phase 2 10
Total 10

Total Credits 162

@’
Hands-on’ Project-based Lab.

*Professional Elective - Electives categorised under Engineering, Science, Mathematics, Live-in-Labs, and
NPTEL Courses. Student can opt for such electives across departments/campuses. Students with CGPA of
7.0 and above can opt for a maximum of 2 NPTEL courses with the credits not exceeding 8.

** Free Electives - This will include courses offered by Faculty of Humanities and Social Sciences/ Faculty
Arts, Commerce and Media / Faculty of Management/Amrita Darshanam -(International Centre for
Spiritual Studies).

*** Live-in-Labs - Students undertaking and registering for a Live-in-Labs project, can be exempted from
registering for an Elective course in the higher semester.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 PROFESSIONAL ELECTIVES

Cat Code Title LTP Cr

ENGG 22AIE431 Applied Cryptography 203 3
ENGG 22AIE432 Network and Wireless Security 203 3
ENGG 22AIE433 Intrusion Detection and Prevention Systems 203 3
ENGG 22AIE434 Software Vulnerability Analysis 203 3
ENGG 22AIE435 Cybercrime Forensics and Digital Forensics 203 3
ENGG 22AIE436 Distributed System Security 203 3
ENGG 22AIE437 Medical Image Processing 203 3
ENGG 22AIE438 Biomedical Signal Processing 203 3
ENGG 22AIE439 Clinical Information Systems 203 3
ENGG 22AIE440 Kinematics and Kinetics for Robotics 203 3
ENGG 22AIE441 Dynamics and Control of Robotics 203 3
ENGG 22AIE442 Robotic Operating Systems & Robot Simulation 203 3
ENGG 22AIE443 Underactuated Robotics 203 3
ENGG 22AIE444 Probabilistic Robotics 203 3
ENGG 22AIE445 Sensors and Actuators for Robotics 203 3
ENGG 22AIE446 NLP for Robotics 203 3
ENGG 22AIE447 Data Driven Control in Robotics 203 3
ENGG 22AIE448 Introduction to Drones 203 3
ENGG 22AIE449 Introduction to Digital Manufacturing 203 3
ENGG 22AIE450 Speech Processing 203 3
ENGG 22AIE451 Modern & Smart Materials 203 3
ENGG 22AIE452 Data Driven Material Modelling & Simulation 203 3
ENGG 22AIE453 Computational Drug Design 203 3
22AIE454 Deep learning in Genomics and
ENGG 203 3
Biomedicine
ENGG 22AIE455 DNA Sequencing Technologies 203 3
ENGG 22AIE456 CRISPR Technology 203 3
ENGG 22AIE457 Full Stack Development 203 3
ENGG 22AIE458 Mobile Application Development 203 3
ENGG 22AIE459 User Experience Design 203 3
ENGG 22AIE460 Software Design Patterns 203 3
ENGG 22AIE461 Concurrent Programming 203 3
ENGG 22AIE462 Deep Reinforcement Learning 203 3
ENGG 22AIE463 Time Series Analysis 203 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022
PROFESSIONAL ELECTIVES UNDER SCIENCE STREAM

CHEMISTRY
Cat. Code Title LTP Credit
SCI 19CHY243 Computational Chemistry and Molecular Modelling 300 3
SCI 19CHY236 Electrochemical Energy Systems and Processes 300 3
SCI 19CHY240 Fuels and Combustion 300 3
SCI 19CHY232 Green Chemistry and Technology 300 3
SCI 19CHY239 Instrumental Methods of Analysis 300 3
SCI 19CHY241 Batteries and Fuel Cells 300 3
SCI 19CHY242 Corrosion Science 300 3
PHYSICS
SCI 19PHY340 Advanced Classical Dynamics 300 3
SCI 19PHY342 Electrical Engineering Materials 300 3
SCI 19PHY331 Physics of Lasers and Applications 300 3
SCI 19PHY341 Concepts of Nanophysics and Nanotechnology 300 3
SCI 19PHY343 Physics of Semiconductor Devices 300 3
SCI 19PHY339 Astrophysics 300 3
Mathematics
SCI 19MAT341 Statistical Inference 300 3
SCI 19MAT342 Introduction to Game Theory 300 3
SCI 19MAT343 Numerical Methods and Optimization 300 3

FREE ELECTIVES

FREE ELECTIVES OFFERED UNDER MANAGEMENT STREAM

Cat. Code Title LTP Credit
HUM 19MNG331 Financial Management 300 3
HUM 19MNG332 Supply Chain Management 300 3
HUM 19MNG333 Marketing Management 300 3
HUM 19MNG334 Project Management 300 3
HUM 19MNG335 Enterprise Management 300 3
HUM 19MNG338 Operations Research 300 3
HUM 19MEE401 Industrial Engineering 300 3
HUM 19MEE346 Managerial Statistics 300 3
HUM 19MEE347 Total Quality Management 300 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 HUM 19MEE342 Lean Manufacturing 300 3
HUM 19CSE358 Software Project Management 300 3
HUM 19CSE359 Financial Engineering 300 3
HUM 19CSE360 Engineering Economic Analysis 300 3
HUM 19MNG331 Financial Management 300 3
HUM 19CSE362 Information Systems 300 3

FREE ELECTIVES OFFERED UNDER HUMANITIES / SOCIAL SCIENCE STREAMS

Cat. Code Title LTP Credit

HUM 19CUL230 Achieving Excellence in Life - An Indian Perspective 200 2
HUM 19CUL231 Excellence in Daily Life 200 2
HUM 19CUL232 Exploring Science and Technology in Ancient India 200 2
HUM 19CUL233 Yoga Psychology 200 2
HUM 19ENG230 Business Communication 103 2
HUM 19ENG231 Indian Thought through English 200 2
HUM 19ENG232 Insights into Life through English Literature 200 2
HUM 19ENG233 Technical Communication 200 2
HUM 19ENG234 Indian Short Stories in English 200 2
HUM 19FRE230 Proficiency in French Language (Lower) 200 2
HUM 19FRE231 Proficiency in French Language (Higher) 200 2
HUM 19GER230 German for Beginners I 200 2
HUM 19GER231 German for Beginners II 200 2
HUM 19GER232 Proficiency in German Language (Lower) 200 2
HUM 19GER233 Proficiency in German Language (Higher) 200 2
HUM 19HIN101 Hindi I 200 2
HUM 19HIN111 Hindi II 200 2
HUM 19HUM230 Emotional Intelligence 200 2
HUM 19HUM231 Glimpses into the Indian Mind - the Growth of Modern India 200 2
HUM 19HUM232 Glimpses of Eternal India 200 2
HUM 19HUM233 Glimpses of Indian Economy and Polity 200 2
HUM 19HUM234 Health and Lifestyle 200 2
HUM 19HUM235 Indian Classics for the Twenty-first Century 200 2
HUM 19HUM236 Introduction to India Studies 200 2
HUM 19HUM237 Introduction to Sanskrit Language and Literature 200 2
HUM 19HUM238 National Service Scheme 200 2
HUM 19HUM239 Psychology for Effective Living 200 2
HUM 19HUM240 Psychology for Engineers 200 2
HUM 19HUM241 Science and Society - An Indian Perspective 200 2
HUM 19HUM242 The Message of Bhagwad Gita 200 2
HUM 19HUM243 The Message of the Upanishads 200 2
HUM 19HUM244 Understanding Science of Food and Nutrition 200 2

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 HUM 19JAP230 Proficiency in Japanese Language (Lower) 200 2
HUM 19JAP2313 Proficiency in Japanese Language (Higher) 200 2
HUM 19KAN101 Kannada I 200 2
HUM 19KAN111 Kannada II 200 2
HUM 19MAL101 Malayalam I 200 2
HUM 19MAL111 Malayalam II 200 2
HUM 19SAN101 Sanskrit I 200 2
HUM 19SAN111 Sanskrit II 200 2
HUM 19SWK230 Corporate Social Responsibility 200 2
HUM 19SWK231 Workplace Mental Health 200 2
HUM 19TAM101 Tamil I 200 2
HUM 19TAM111 Tamil II 200 2

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 SYLLABUS

SEMESTER 1

22MAT110 MATHEMATICS FOR COMPUTING 1 L-T-P-C: 2- 1- 3- 4

Course Objectives

 The course will lay down the basic concepts and techniques of linear algebra, calculus, and basic
probability theory needed for subsequent study
 It will explore the concepts initially through computational experiments and then try to understand the
concepts/theory behind them.
 At the same time, it will provide an appreciation of the wide application of these disciplines within the
scientific field
 Another goal of the course is to provide the connection between the concepts of linear algebra,
differential equations, and probability theory.

Course Outcomes
After completing this course, students will be able to
CO1: Apply the concepts of linear algebra to solve canonical problems.
CO2: Model simple physical systems using ordinary differential equations.
CO3: Solve elementary problems using the concepts of probabilistic theory.
CO4: Analyze elementary problems in linear algebra, ODE, and probabilistic theory with computational
techniques.

CO-PO Mapping

PO/P
PO PO PO PO PO PO PSO
SO PO2 PO3 PO4 PO5 PO7 PO8 PSO2 PSO3
1 6 9 10 11 12 1
CO
CO1 3 3 1 - 3 - - - 2 2 - 2 3 - -
CO2 3 3 1 - 3 - - - 2 2 - 2 3 - -
CO3 3 3 1 - 3 - - - 2 2 - 2 3 - -
CO4 3 2 2 - 3 - - - 2 2 - 2 3 1 -

Syllabus
Unit 1
Basics of Linear Algebra - Linear Dependence and independence of vectors - Gaussian Elimination - Rank of set
of vectors forming a matrix - Vector space and Basis set for a Vector space - Dot product and Orthogonality -
Rotation matrices - Eigenvalues and Eigenvectors and its interpretation.

Unit 2
Ordinary Linear differential equations, formulation, analytical and Numerical solutions, Impulse Response
Computations, Converting higher order into first order equations. Examples of ODE modelling in falling objects,
satellite and planetary motion, Electrical and mechanical systems. Multivariate calculus, Taylor series.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Unit 3
Introduction to random variables (continuous and discrete), mean, standard deviation, variance, probability
distributions and Monte Carlo Simulations.

Text Books:

 Gilbert Strang, Introduction to Linear Algebra, Fifth Edition, Wellesley-Cambridge Press, 2016.
 Gilbert Strang, Linear Algebra and Learning from Data, Wellesley, Cambridge press, 2019.
 William Flannery, Mathematical Modelling and Computational Calculus, Vol-1, Berkeley Science Books,
2013.
 Douglas C. Montgomery and George C. Runger, Applied Statistics and Probability for Engineers, (2005)
John Wiley and Sons Inc.
References:
 Stephen Boyd and Lieven Vandenberghe, Introduction to Applied Linear Algebra – Vectors, Matrices,
and Least Squares, 2018.
 Papoulis, and Unnikrishna Pillai, “Probability, Random Variables and Stochastic Processes”, Fourth
Edition, McGraw Hill, 2002.
 D. Bertsekas and J. Tsitsiklis, Introduction to Probability, 2nd Edition, Athena Scientific, 2008.
Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 3) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22PHY106 COMPUTATIONAL PHYSICS L-T-P-C: 2- 0- 3- 3

Course Objectives

 The course will lay down the basic concepts and techniques needed for verticals such as robotics.
 It will explore the concepts initially through computational experiments and then try to understand the
concepts/theory behind them.
 It will help the students to perceive the engineering problems using the fundamental concepts in physics.
 Another goal of the course is to provide the connection between the concepts of physics, mathematics,
and computational thinking.

Course Outcomes

After completing this course, students will be able to

CO1: Apply the principles of statics to solve elementary problems in physics.

CO2: Apply computational techniques to solve elementary problems in statics.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO3: Apply computational techniques to solve elementary problems in dynamics.

CO4: Analyze the motion of rigid bodies by applying fundamental principles of dynamics.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

CO

CO1 3 2 - - 3 - - - 2 2 - 2 3 - -

CO2 3 2 - - 3 - - - 2 2 - 2 3 - -

CO3 3 2 - - 3 - - - 2 2 - 2 3 - -

CO4 3 2 2 - 3 - - - 2 2 - 2 3 - 1

Syllabus

Unit 1

Newton’s Laws of Motion, Force as 3D Vector, Resolution of Forces, Resultant of Forces.

Unit 2
Equilibrium about a Point, Moment, Couple, Equivalent System, Equilibrium of Rigid Bodies, Degree-of-freedom
and Constraints at Supports, Free Body Diagram.

Unit 3
Kinematics of particles, assumptions, Cartesian, Cylindrical and Spherical frames, and motion of particles in them.
Translation and rotation of rigid bodies in 2D – Translation and rotation of rigid bodies in 3D.

Unit 4
Kinematics of interconnected rigid bodies– Definition of a linkage – Definition of a mechanism –Four-bar
mechanism.

Textbooks

 Merlam J.L and Kraige L.G., Engineering Mechanics, Volume I - statics, Volume 11- dynamics, John
Wiley & Sons, New York, 2018.
 Hibbeler R. C., Engineering Mechanics: Statics and Dynamics, 11th edition, Pearson Education India,
2017.
 Elementary Mechanics Using Matlab – Malthe & Sorenssen – Undergraduate Lecture Notes in
Physics, Springer International Publishing, 2015.
 Elementary Mechanics Using Python – Malthe & Sorenssen – Undergraduate Lecture Notes in
Physics, Springer International Publishing, 2015.

References Books

 Beer F.P. and Johnston E.R., Vector Mechanics for Engineers - Volume I - Statics, Volume II -
Dynamics, McGraw Hill, New York, 2004.
 Shames I. H., Engineering Mechanics, Prentice HaII, New Delhi, 1996.
 Statics with Matlab – Marghitu, Dupac& Madsen, Springer – Verlag London 2013.
 Advanced Dynamics - Marghitu, Dupac& Madsen, Springer – Verlag London 2013.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  Dukkipati R. V., MATLAB: An Introduction with Applications, New Age International; 2010.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE101 PROBLEM SOLVING & C PROGRAMMING L-T-P-C: 2- 1- 3- 4

Course Objectives
 To understand the various steps in Program development.
 To understand the basic concepts in C Programming Language.
 To learn how to write modular and readable C Programs.
 To imbibe the problem-solving strategy skill through C programming.

Course Outcomes

After completing this course, students will be able to

CO1: Implement simple algorithms for arithmetic and logical problems to translate pseudocode in C language.
CO2: Evaluate the programs to correct syntax and logical errors.
CO3: Synthesize a complete program using problem solving strategy.
CO4: Apply programming to solve matrix addition and multiplication problems and searching and sorting
problems.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 2 - - - 3 - - - 3 3 - 3 3 3 -

CO2 - - - - - - - - 3 3 - 3 - 3 -

CO3 3 3 - - - - - 3 3 - 3 3 3 -

CO4 3 2 3 - 3 - - - 3 3 - 3 3 3 -

Syllabus

Unit 1
Introduction to problem-solving- Computation– expressions, logic; pseudocode vs programs, Problem
Understanding and Analysis – problem definition, input-output, variables, name binding, the idea of algorithms,
problem-solving strategy, Introduction to Programming language concepts, machine language, flowcharts/Pseudo
codes, types of compilers and software, pseudocode to programs.

Unit 2
Introduction to C programming, Structure of a C program, Data type, Constants, Variables, Identifiers, Keywords,
Declarations, Expressions, Statements, and Symbolic constants.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Input and Output: getchar, putchar, scanf, printf, gets, puts, functions, Pre-processor commands, Preparing and
running a complete C program.
Operators and expressions: Arithmetic, unary, logical, bit-wise, assignment and conditional operators, Library
functions.
Control statements: if-else, switch, break, continue, while, do-while, for statements, nested loops, goto statements,
comma operator.

Unit 3
Functions: Defining and accessing function, passing arguments, function prototypes, recursion, use of library
functions, and storage classes.
Arrays: Defining and processing an array, Passing array to a function, multi-dimensional arrays, Sequential search,
Sorting arrays, String handling, Operations on strings,
Pointers: Declarations, Passing pointer to a function, Operations on pointers, Pointers and arrays, Arrays of
pointers.
Structures and unions: Defining and processing a structure, passing structure to a function, Pointers; and Unions.

Unit 4
File handling: Open, Close, Create, File operations, Unformatted data files, Command line arguments. The
Standard C Pre-processor: Defining and calling macros, utilizing conditional compilation, passing values to the
compiler, The Standard C Library: Input/Output: fopen, fread, etc, string handling functions, Math functions: log,
sin, alike Other Standard C functions.

Textbooks
 Forouzan BA, Gilberg RF. Computer Science: A structured programming approach using C. Third
Edition, Cengage Learning; 2006.
Reference Books
 Ferragina P, Luccio F. Computational Thinking: First Algorithms, Then Code. Springer; 2018.
 Beecher K. Computational Thinking: A beginner's guide to Problem-solving and Programming. BCS
Learning & Development Limited; 2017.
 Byron Gottfried. Programming With C. Fourth Edition, McGrawHill; 2018.
 Kanetkar, Yashavant, Let us C, BPB publications, 2018.
 Brian W. Kernighan and Dennis M. Ritche, The C Programming Language, Pearson Publication, 2015
 Problem Solving and Program Design in C, J. R. Hanly and E. B. Koffman, 5th Edition, Pearson
Education.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 3) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE102 ELEMENTS OF COMPUTING SYSTEMS 1 L-T-P-C: 2- 0- 3- 3

Course Objectives
• The course will expose the students to the basics of Boolean algebra and it will further help them to understand
the workings of a modern computer.

• Students will be trained to build a computing system using elementary logic gates such as NAND, AND, OR
etc. through simulation software.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Course Outcomes

After completing this course, the students will be able to

CO1: Realize the concept of Boolean Algebra and Digital Logic.
CO2: Implement different combinational and sequential digital logic systems.
CO3: Design the hardware hierarchy of general-purpose computing systems.
CO4: Build a general-purpose computer capable of running stored programs written in the machine language.

CO – PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO

CO1
3 3 2 2 3 - - - 3 2 3 3 - - -
CO2
3 3 3 3 3 2 - - 3 2 3 3 1 2 -
CO3
3 2 3 3 3 - - - 3 2 3 3 - 2 -
CO4
3 2 3 2 3 - - - 3 2 3 3 - 2 -

Syllabus
Unit 1
Number System-Decimal to Binary Conversion- Negative Numbers- Signed Magnitude Number System- Boolean
algebra and Karnaugh Maps-Boolean Logic, -Logic Gates-Introduction to Hardware simulator platforms; Nand
ToTetris, -Hardware description language-Realization of basic gates using NAND gate.

Unit 2
Boolean function synthesis-Combinational Logic- Half Adder-Full Adder-Multiplexer (MUX) and demultiplexer
(DeMUX) design-ALU and its implementation.

Unit 3
Sequential Logic Design- Memory Elements Computer Architecture: Von-Neumann architecture-Registers-Flip-
Flops-RAM, ROM, Program Counter -Hack CPU -Machine Language vs High-level- Basic experiments using
machine language.

Text Books:
1. Noam Nisan and Shimon Schocken, “Elements of Computing Systems”, MIT Press, 2012.
2. M. Morris Mano, “Digital Design”, 5th Edition, Pearson Education (Singapore) Pvt. Ltd., New
Delhi,2014.
3. John.M Yarbrough, “Digital Logic Applications and Design”, Thomson Learning, 2006.

Reference Books:
4. Anil K. Maini, “Digital Electronics”, Wiley, 2014.
5. Thomas L. Floyd, “Digital Fundamentals”, 10th Edition, Pearson Education Inc, 2011.
6. Donald D.Givone, “Digital Principles and Design”, TMH, 2003.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22MAT121 DISCRETE MATHEMATICS L-T-P-C: 2- 0- 3- 3

Course Objectives

 Familiar various concepts in logic and proof techniques.

 Understand the concepts of various types of relations, partial ordering and equivalence relations.
 Understand the concepts of generating functions and apply to solve the recurrence relations.
 Familiar basic results in number theory and understand it applications in information security.

Course Outcomes
After completing this course, the students will be able to

CO1: Apply the tools and techniques of mathematical reasoning required for computing.
CO2: Apply the concepts of generating functions to solve the recurrence relations.
CO3: Apply the concepts of divide and conquer method and principle of inclusion and exclusion to solve
some simple algorithms in discrete mathematics.
CO4: Apply the formalism of number theory required for computing.

CO-PO Mapping

PO/P
PO PO PO PO PO PO PSO
SO PO2 PO3 PO4 PO5 PO7 PO8 PSO2
1 6 9 10 11 12 1
CO
CO1 3 2 1 - - - - - - - - - 2 1
CO2 3 3 2 - - - - - - - - - - 2
CO3 3 3 2 - - - - - - - - - 1 -
CO4 2 3 2 - - - - - - - - - 1 2

Syllabus

Unit 1
Logic, Mathematical Reasoning and Counting: Logic, Prepositional Equivalence, Predicate and Quantifiers,
Theorem Proving, Functions, Mathematical Induction. Recursive Definitions, Recursive Algorithms, Basics of
Counting, Pigeonhole Principle, Permutation and Combinations.

Unit 2
Relations and Their Properties: Representing Relations, Closure of Relations, Partial Ordering, Equivalence
Relations, and partitions. Advanced Counting Techniques and Relations: Recurrence Relations, Solving
Recurrence Relations, Generating Functions, Solutions of Homogeneous Recurrence Relations, Divide and
Conquer Relations, Inclusion-Exclusion.

Unit 3
Number Theory: Divisibility and Factorization. Simultaneous linear congruences, Chinese Remainder Theorem.
Wilson's Theorem, Fermat's Theorem, pseudoprimes and Carmichael numbers, Euler's Theorem. Arithmetic
functions and Quadratic residues.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Textbooks:
7. Kenneth H. Rosen, “Discrete Mathematics and its Applications”, Tata McGraw- Hill Publishing
Company Limited, New Delhi, Sixth Edition, 2007.
8. James Strayer, Elementary Number Theory, Waveland Press, 2002.

Reference(s)
9. R.P. Grimaldi, “Discrete and Combinatorial Mathematics”, Pearson Education, Fifth Edition,2007.
10. Thomas Koshy, “Discrete Mathematics with Applications”, Academic Press, 2005.
11. Liu, “Elements of Discrete Mathematics”, Tata McGraw- Hill Publishing Company Limited , 2004.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22ADM101 Foundations of Indian Heritage L-T-P-C: 2-0-0-2

Course Objectives

 The course is designed as an introductory guide to the variegated dimensions of Indian cultural and
intellectual heritage, to enable students to obtain a synoptic view of the grandiose achievements of India
in diverse fields.
 It will equip students with concrete knowledge of their country and the mind of its people and instil in
them some of the great values of Indian culture.

Course Outcomes
After completing this course, students will be able to

CO1: Be introduced to the cultural ethos of Amrita Vishwa Vidyapeetham, and Amma’s life and vision of
holistic education.
CO2:Understand the foundational concepts of Indian civilization like puruśārtha-s, law of karma and
varṇāśrama.
CO3:Gain a positive appreciation of Indian culture, traditions, customs and practices.
CO4: Imbibe spirit of living in harmony with nature, and principles and practices of Yoga.
CO5:Get guidelines for healthy and happy living from the great spiritual masters

CO-PO Mapping

PO/PSO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
CO
CO1 3 2 3 2
CO2 3 1 3 2
CO3 3 1 3 2
CO4 3 3 3 2

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO5 3 1 3 2

Syllabus

Unit 1
Introduction to Indian culture; Understanding the cultural ethos of Amrita Vishwa Vidyapeetham; Amma’s life
and vision of holistic education.
Unit 2
Goals of Life – Purusharthas; Introduction to Varnasrama Dharma; Law of Karma; Practices for Happiness.
Unit 3
Symbols of Indian Culture; Festivals of India; Living in Harmony with Nature; Relevance of Epics in Modern
Era; Lessons from Ramayana; Life and Work of Great Seers of India.

Text Book
 Cultural Education Resource Material Semester-1

Reference Book(s)
 The Eternal Truth (A compilation of Amma’s teachings on Indian Culture)
 Eternal Values for a Changing Society. Swami Ranganathananda. BharatiyaVidyaBhavan.
 Awaken Children (Dialogues with Mata Amritanandamayi) Volumes 1 to 9
 My India, India Eternal. Swami Vivekananda. Ramakrishna Mission.

Evaluation Pattern:

Assessment Internal End

Semester
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.

19ENG111 TECHNICAL COMMUNICATION L-T-P-C: 2-0-3-3

Course Objectives:

 To introduce the students to the fundamentals of mechanics of writing

 To facilitate them with the style of documentation and specific formal written communication
 To initiate in them the art of critical thinking and analysis
 To help them develop techniques of scanning for specific information, comprehension and organization
of ideas
 To enhance their technical presentation skills
Course Outcomes: The course will enable the student:

CO1: To gain knowledge about the mechanics of writing and the elements of formal correspondence

CO2: To understand and summarise technical documents

CO3: To apply the basic elements of language in formal correspondence

CO4: To interpret and analyze information and to organize ideas in a logical and coherent manner

CO5: To compose project reports/ documents, revise them for language accuracy and make technical
presentations

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO-PO Mapping

Course P PO PO PO PO PO PO PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

Outcomes O 2 3 4 5 6 7
1
CO1 3
CO2 1 2
CO3 3
CO4 1 2
CO5 2 1

Syllabus

Unit 1
Mechanics of Writing: Grammar rules -articles, tenses, auxiliary verbs (primary & modal) prepositions, subject-
verb agreement, pronoun-antecedent agreement, discourse markers and sentence linkers

General Reading and Listening comprehension - rearrangement & organization of sentences

Unit 2
Different kinds of written documents: Definitions- descriptions- instructions-recommendations- user manuals -
reports – proposals

Formal Correspondence: Writing formal Letters

Mechanics of Writing: impersonal passive & punctuation

Scientific Reading & Listening Comprehension

Unit 3

Technical paper writing: documentation style - document editing – proof reading - Organising and formatting

Mechanics of Writing: Modifiers, phrasal verbs, tone and style, graphical representation

Reading and listening comprehension of technical documents

Mini Technical project (10 -12 pages)

Technical presentations
Text Books & References
1. Hirsh, Herbert. L “Essential Communication Strategies for Scientists, Engineers and Technology
Professionals”. II Edition. New York: IEEE press, 2002
2. Anderson, Paul. V. “Technical Communication: A Reader-Centred Approach”. V Edition.
Harcourt Brace College Publication, 2003
3. Strunk, William Jr. and White. EB. “The Elements of Style” New York. Alliyan& Bacon, 1999.
4. Riordan, G. Daniel and Pauley E. Steven. “Technical Report Writing Today” VIII Edition (Indian
Adaptation). New Delhi: Biztantra, 2004.
5. Michael Swan. ‘’ Practical English Usage’’, Oxford University Press, 2000

Evaluation Pattern

Assessment Internal/External Weightage (%)

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Periodical 1 Internal 10

Periodical 2 Internal 10

*Continuous Assessment (Theory) (CAT) Internal 10

*Continuous Assessment (Lab) (CAL) Internal 40

End Semester External 30

*CA can be Quizzes, Assignments, Projects and Report

SEMESTER II

22MAT122 MATHEMATICS FOR COMPUTING 2 L-T-P-C: 2- 1- 3- 4

Course Objectives

 The course will lay down the basic concepts and techniques of linear algebra, calculus and basic
probability theory needed for subsequent study.
 It will explore the concepts initially through computational experiments and then try to understand the
concepts/theory behind it.
 At the same time, it will provide an appreciation of the wide application of these disciplines within the
scientific field.
 Another goal of the course is to provide connection between the concepts of linear algebra, differential
equation and probability theory.

Course Outcomes
After completing this course student will be able to,

CO1: Apply matrix decomposition techniques to solve elementary problems.

CO2: Apply the concepts of linear algebra and differential calculus to solve elementary optimization
problems.
CO3: Analyze data using fundamental techniques of probability.
CO4: Implement the concepts and techniques of linear algebra, optimization and probability for signal and
image processing.

CO-PO Mapping

PO/
PSO PO PO PO PO PO PO PO PO PO PO PO PSO PSO
PO7 PSO1
1 2 3 4 5 6 8 9 10 11 12 2 3
CO
CO1 3 2 1 1 3 - - - 2 2 - 2 3 - 1
CO2 3 2 1 1 3 - - - 2 2 - 2 3 - 1
CO3 3 2 1 1 3 - - - 2 2 - 2 3 - 1
CO4 3 2 1 1 3 - - - 2 2 - 2 3 1 1

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Syllabus
Unit 1
Gaussian elimination, LU decomposition, Infinite dimensional vector spaces, Fourier Series and Fourier
Transform and its properties, Convolution, Vector spaces associated with Matrices, Projection matrix and
Regression, Convolution sum, Convolution Integral, Cayley Hamilton theorem, Diagonalizability of matrices,
Eigenvalues and Eigenvectors of Symmetric matrices, Eigenvalues and Eigen vectors of ATA, AAT, Relationship
between vector spaces associated with A, ATA, AAT, Singular Value Decomposition.

Unit 2
Taylor series expansion of multivariate functions, conditions for maxima, minima and saddle points, Concept of
gradient and hessian matrices, Multivariate regression and regularized regression. Theory of convex and non-
convex optimization, Newton method for unconstrained optimization. Signal processing with regularized
regression.

Unit 3
Random variables and distributions, Expectation, Variance, Moments, Cumulants, Sampling from univariate
distribution- various methods, Bayes theorem, Concept of Jacobian, and its use in finding pdf of functions of
Random variables (RVs), box-muller formula for sampling normal distribution, Concept of correlation and
Covariance of two linearly related RVs.

Text Books:

 Gilbert Strang, Linear Algebra and Learning from Data, Wellesley, Cambridge press, 2019.
 William Flannery, “Mathematical Modeling and Computational Calculus”, Vol-1, Berkeley Science
Books, 2013.
 Stephen Boyd and Lieven Vandenberghe, "Convex Optimization“, Cambridge University Press, 2018.
 Douglas C. Montgomery and George C. Runger, Applied Statistics and Probability for Engineers,
(2005) John Wiley and Sons Inc.

Reference Books:
 Stephen Boyd and Lieven Vandenberghe, “Introduction to Applied Linear Algebra – Vectors, Matrices,
and Least Squares", Cambridge University Press, 2018.
 Papoulis, and Unnikrishna Pillai, “Probability, Random Variables and Stochastic Processes”, Fourth
Edition, McGraw Hill, 2002.
 Introduction to Probability, D. Bertsekas and J. Tsitsiklis, 2nd Edition, Athena Scientific, 2008.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 3) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE111 OBJECT ORIENTED PROGRAMMING IN JAVA L-T-P-C: 2- 1- 3- 4

Course Objectives

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  The course will provide an introduction to object-oriented programming.
 It will expose the students to the paradigm of object-oriented programming.
 Students will also be motivated to solve the problems in engineering using the concepts of object-
oriented
programming.

Course Outcomes
After completing this course, students will be able to

CO1: Represent the problems using objects and classes.

CO2: Implement object-oriented concepts using the Java language.
CO3: Apply the object-oriented concepts to design and visualize programs using UML.
CO4: Implement applications using object-oriented features.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

CO

CO1 3 3 2 2 3 - - - 3 2 3 3 1 1 1

CO2 3 3 3 3 3 - - - 3 2 3 3 1 1 1

CO3 3 2 3 3 3 - - - 3 2 3 3 1 1 1

CO4 3 2 3 3 3 - - - 3 2 1 3 1 1 1

Syllabus

Unit 1:
Introduction: Introduction to Java Language and Runtime Environment, JVM, Bytecode, Object-oriented
concepts- Abstraction, Encapsulation, Inheritance and Polymorphism, Basic program syntax, Hello world, Data
types, Variables, Operators, Control statements and functions-value types and reference types, The concept of
references

Unit 2:
Classes, Objects, and Constructors: Objects in Java, Class file, Constructor functions, Class members and
method, Class Instance variables, The Object class, Garbage collector, Method overloading, Constructors,
Constructor overloading.
Inheritance and Packages: Basics of Inheritance, Types of Inheritance, Super keyword, Final keyword,
Overriding of methods, Applying and implementing interfaces, Packages-create, access and importing packages

Unit 3:
Exception handling and Threading: Introduction to exception handling, Hierarchy of exception, Usage of try,
catch, throw, throws and finally, Built-in and user defined exceptions, Threads, Creating Threads, Thread life
cycle, Concept of multithreading

Unit 4:
GUI programming with Swing: Applets-Applet class, Delegation event model-events, event sources, event
listeners, event classes, mouse and keyboard events, JLabel, JText, JButton, JList, JCombo box.

Textbooks

 Herbert Schildt, Java: A Beginner's Guide, Tata McGraw-Hill Education, Ninth Edition

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Reference Books

 Herbert Schildt, Java The Complete Reference, Tata McGraw-Hill Education, Ninth Edition.
 Sierra, Kathy, and Bert Bates. Head first java. " O'Reilly Media, Inc.", 2003.
 John R. Hubbard, Schaum's Outline of Programming with Java, McGraw-Hill Education, 2004

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 3) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE112 DATA STRUCTURES & ALGORITHMS 1 L-T-P-C: 2- 1- 3- 4

Course Objectives
 This course aims at introducing the concept of data structure hierarchy.
 It will also expose the students to the basic and higher order data structures.
 Further the students will be motivated to apply the concept of data structures to various engineering
problems.

Course Outcomes
After completing this course, the students will be able to

CO1: Apply an appropriate data structure for a specified problem.

CO2: Analyze the complexity of algorithms.
CO3: Implement linear data structures to solve different problems.
CO4: Implement non-linear data structures like trees to solve different problems.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 3 2 2 3 - - - 3 2 3 3 2 1 -
CO2 3 3 3 3 3 - - - 3 2 3 3 3 2 -
CO3 3 2 3 3 3 - - - 3 2 3 3 2 2 2
CO4 3 3 3 2 3 - - - 3 2 3 3 2 3 2
CO-PO
Syllabus

Unit 1
Data Structure Hierarchy – primitive and non-primitive, Array data structure, properties and functions, single and
multi-dimensional arrays, simple problems, Basics of Algorithm Analysis, big-O notation, notion of time and
space complexity, dynamic arrays

Unit 2
Linked List, properties and functions, array implementations, singly linked list, doubly linked list, circular linked
list, properties and functions, simple problems

Unit 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Stack data structure, properties and functions, recursion, expression evaluation, simple problems, Queue data
structure, Circular queue, Double ended queue, priority queues, properties and functions, simple problems
(Implementation using arrays, LL)

Unit 4
Tree – Binary Tree, Binary Search Tree-– Array and Linked list representation, AVL Tree - union and
intersections of tree structures, Complete binary tree, Binary Heap Data Structure-Heap order and Heapsort

Textbooks
 Alfred V Aho, John E Hopcroft, Jeffrey D Ullman. Data Structures & Algorithms, Pearson Publishers,
2002.
 Maria Rukadikar S. Data Structures & Algorithms, SPD Publishers, 2011.

Reference Books
 Michael T. Goodrich & Roberto Tamassia, Data Structures and Algorithms in Java,Wiley India
Edition, Third Edition.
 Narasimha Karumanchi, Data Structures and Algorithms Made Easy in Java, CarrerMonk, 2011
 Y. Langsam, M. Augenstin and A. Tannenbaum, Data Structures using C and C++, Pearson
Education, 2002.
 Lipschutz Seymour, Data Structures with C (Schaum's Outline Series), McGraw Hill Education India,
2004

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 3) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE113 ELEMENTS OF COMPUTING SYSTEMS – 2 L-T-P-C: 2- 0- 3- 3

Course Objectives
• This course is an integrative, project-oriented systems building course.
• The course exposes students to a significant body of computer science knowledge, gained through a series of
hardware and software construction tasks.
• These tasks demonstrate how theoretical and applied techniques in AI are used in practice.

Course Outcomes
After completing this course, students will be able to

CO1: Analyze the important components of a MIPS computer system and the basic organization
CO2: Implement low-level programming on the hardware platform
CO3: Develop programs in object-based language ‘Jack’
CO4: Execute experiments related to basic concepts and functions of operating systems and compilers.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO

CO1 3 3 2 2 3 - - - 3 2 3 3 - - -

CO2 3 3 3 3 3 - - - 3 2 3 3 - 2 -

CO3 3 2 3 3 3 - - - 3 2 3 3 - 2 -

CO4 3 2 3 2 3 - - - 3 2 3 3 - 2 -

Syllabus

Unit 1
Basic Computer Architecture-Instruction set and Machine language-MIPS instructions- add, subtract, bitwise
operators, branches- CPI metric- Data path design for single clock.-Assembler

Unit 2
Virtual Machine I: Stack Arithmetic, Background VM Specification Part-1, Implementation and Perspective.
Virtual Machine II: Program Control Background, VM Specification Part-2, Implementation, Perspective. High-
Level Language: Background, The Jack Language Specification. Writing Jack Applications.Perspective.

Unit 3
Compiler I - Syntax Analysis: Background, Specification, Implementation, Perspective. Compiler II - Code
Generation: Background, Specification, Implementation, Perspective. Operating System: Background, the Jack
OS Specification, Implementation, Perspective

Textbooks:

 Nisan, Noam, and Shimon Schocken. The elements of computing systems: building a modern computer from
first principles. MIT Press, 2005.
 M. Morris Mano Computer System Architecture, Prentice Hall, Third Edition.

Reference Books:

 Hennessy, John L., and David A. Patterson. Computer architecture: a quantitative approach. Elsevier, 5th
Edition, 2011.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 22AIE114 INTRODUCTION TO ELECTRICAL AND ELECTRONICS ENGINEERING L-T-P-C: 2- 0- 3- 3

Course Objectives

 The course will lay down the basic concepts and techniques of electrical and electronics engineering
needed for advanced topics in AI.
 It will help the students to perceive the engineering problems using the fundamental concepts in electrical
and electronics engineering.
 Another goal of the course is to provide connection between the concepts of electrical and electronics
engineering, mathematics, and computational thinking.

Course Outcomes

After completing this course, students will be able to

CO1: Familiarise the fundamental concepts in electrical and electronics engineering.

CO2: Implement the state-of-the-art computational techniques that can be employed to analyse the structured
problems in electrical engineering.
CO3: Realize basic electronic components and circuits using various semiconductor devices
CO4: Implement various circuits applications in the perspective of electronics

CO-PO Mapping

PO/ PO PO PO PO PO5 PO PO PO8 PO PO10 PO1 PO1 PSO PSO PSO

PSO 1 2 3 4 6 7 9 1 2 1 2 3
CO1 3 3 3 3 3 - - - 3 3 2 2 - 2 -
CO2 3 3 3 3 3 - - - 3 3 2 2 2 2 -
CO3 3 3 3 3 3 - - - 3 3 2 2 1 2 -
CO4 3 3 3 3 3 - - - 3 3 1 1 2 2 -
Syllabus

Unit 1
Fundamental electrical laws-Fundamental circuit elements: charge, voltage, current – Resistance – Ohms law –
Kirchhoff’s voltage and current law – Energy and power – Series parallel combination of R, L, C components –
Voltage divider and current divider rules – Super position theorem – Inductors and capacitors – Impedance and
AC sinusoidal signals

Unit 2
Semiconductor materials – PN junction diode – Diode characteristics – Diode applications: Clippers and Clampers
– Rectifiers: Half wave, Full wave, Bridge – Zener diode –Introduction to BJT–BJT characteristics and
configurations – CE amplifier – Transistor as a switch – Filed effect transistors: MOSFET

Unit 3
Operational amplifiers – Inverting and non-inverting amplifier – Oscillators –Instrumentation amplifier

Textbooks:
1. Hughes, Edward, John Hiley, Ian McKenzie Smith, and Keith Brown. Hughes electrical and electronic
technology. Pearson education, 2005.
2. David A. Bell. Electronic Devices and Circuits, 5th Edition, Oxford University Press, 2008.
3. Bhattacharya, S. K. Basic Electrical Engineering. Pearson Education India, 2011.

Reference Books:

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  A. Malvino And D. J. Bates. Electronic Principles, 7th Edition, Tata McGraw - Hill, 2007.
 Vincent Del Toro. Electrical Engineering Fundamentals, Prentice Hall of India Private Limited, 2nd
Edition, 2003.
 Michael Tooley B. A. Electronic circuits: Fundamentals and Applications, 3rd Edition, Elsevier Limited,
2006

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE115 USER INTERFACE DESIGN L-T-P-C: 2- 0- 3- 3

Course Objectives

 Focus in this course is on the basic understanding of user interface design by applying HTML, CSS and
Java Script.

 On the completion of the course, students will be able to develop basic web applications

 This course will serve as the foundation for students to do several projects and other advanced courses
in computer science

Course Outcomes
After completing this course, students will be able to

CO1: Apply the basics of World Wide Web concepts during web development.
CO2: Develop webpage GUI using HTML5 technology.
CO3: Develop GUI using CSS and Java Script.
CO4: Develop a simple web application using html, CSS and JavaScript.

CO-PO Mapping

PSO/P PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PS PS PS

O 1 2 3 4 5 6 7 8 9 0 1 2 O1 O2 O3
CO
CO1 2 2 2 - - 2 2 - - - - - - - 2
CO2 2 2 2 - - - - - - 3 - - - - -
CO3 2 2 2 - - - - 1 - 3 - - - 1 1
CO4 - 2 3 3 3 2 2 3 3 3 2 - 1 2 3
CO5 2 2 3 2 3 - - 3 2 - - - - 2 3

Syllabus

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Unit 1
Introduction to Web – Client/Server - Web Server - Application Server- HTML Basics- Tags - Adding Web
Links and Images- Creating Tables-Forms - Create a Simple Web Page - HTML 5 Elements - Media – Graphics.

Unit 2
CSS Basics –Features of CSS – Implementation of Borders - Backgrounds- CSS3 - Text Effects -Fonts -Page
Layouts with CSS

Unit 3
Introduction to Java Script –Form Validations – Event Handling – Document Object Model - Deploying an
application

Textbooks

 Kogent Learning Solutions Inc. Html5 Black Book: Covers Css3, Javascript, Xml, Xhtml, Ajax,
PhpAndJquery. Second Edition, Dreamtech Press; 2013.
Reference Books

 Tittel E, Minnick C. Beginning HTML5 and CSS3 For Dummies. Third edition, John Wiley & Sons;
2013.
 Powell TA, Schneider F. JavaScript: the complete reference. Paperback edition, Tata McGraw-Hill;
2012.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22ADM111 Glimpses of Glorious India L-T-P-C: 2-0-0-2

Course Objectives
 To deepen students’ understanding and further their knowledge about the different aspects of Indian
culture and heritage.
 To in still into students a dynamic awareness and understanding of their country’s achievements and
civilizing influences in various fields and at various epochs.

Course Outcome

CO1: Get an overview of Indian contribution to the world in the field of science and literature.
CO2: Understand the foundational concepts of ancient Indian education system.
CO3: Learn the important concepts of Vedas and Yogasutra-s and their relevance to daily life.
CO4: Familiarize themselves with the inspirational characters and anecdotes from the Mahābhārata and
Bhagavad
Gītā and Indian history.
CO5: Gain an understanding of Amma’s role in the empowerment of women.

CO-PO Mapping

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 PO/PSO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 3 2
CO2 1 3 2
CO3 3 3 3 2
CO4 3 3 3 2
CO5 1 1
Syllabus

Unit 1
To the World from India; Education System in India; Insights from Mahabharata; Human Personality. India’s
Scientific System for Personality Refinement.

Unit 2
The Vedas: An Overview; One God, Many Forms; Bhagavad Gita – The Handbook for Human Life; Examples
of Karma Yoga in Modern India.

Unit 3
Chanakya’s Guidelines for Successful Life; Role of Women; Conservations with Amma.

Text Book
 Cultural Education Resource Material Semester-2
Reference Book(s)
 Cultural Heritage of India. R.C.Majumdar. Ramakrishna Mission Institute of Culture.
 The Vedas. Swami ChandrashekharaBharati. BharatiyaVidyaBhavan.
 Indian Culture and India’s Future. Michel Danino. DK Publications.
 The Beautiful Tree. Dharmapal. DK Publications.
 India’s Rebirth. Sri Aurobindo. Auroville Publications.

Evaluation Pattern:

Assessment Internal End

Semester
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports

SEMESTER III

22MAT220 MATHEMATICS FOR COMPUTING 3 L-T-P-C: 2- 1- 3- 4

Course Objectives

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  The course will lay down the basic concepts and techniques of linear algebra, calculus and basic
probability theory needed for subsequent study.
 It will explore the concepts initially through computational experiments and then try to understand the
concepts/theory behind it.
 At the same time, it will provide an appreciation of the wide application of these disciplines within the
scientific field.
 Another goal of the course is to provide connection between the concepts of linear algebra, differential
equation and probability theory.

Course Outcomes
After completing this course, students will be able to
CO1: Demonstrate the techniques of optimization needed for AI.
CO2: Analyze physical systems using the formalism of partial differential equation.
CO3: Use the tools and techniques of probability theory needed for data analysis.
CO4: Apply modern computational tools and techniques for solving advanced problems in optimization,
differential calculus, and probability theory needed for AI.

CO-PO Mapping

PO/PS
O PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO
1 2 3 4 5 6 7 8 9 0 1 2 1 2 3
CO
CO1 3 3 2 3 3 - - - 3 2 2 3 3
CO2 3 3 2 3 3 - - - 3 2 2 3 2
CO3 3 3 2 3 3 - - - 3 2 2 3 3
CO4 3 3 2 3 3 - - - 3 2 2 3 3

Syllabus

Unit 1
Direct methods for convex functions, sparsity inducing penalty functions. Constrained Convex Optimization
problems, Krylov subspace, Conjugate gradient method, formulating problems as LP and QP, support vector
machines, solving by packages (CVXOPT), Lagrangian multiplier method, KKT conditions. Introduction to
alternating direction method of multipliers (ADMM) - the algorithm. Applications in signal processing, pattern
recognition and classification.

Unit 2
Introduction to PDEs. Formulation and numerical solution methods (Finite difference and Fourier) for PDEs in
Physics and Engineering.

Unit 3
Inequalities of statistics, Multivariate Gaussian and weighted least squares, Markov chains, Markov decision
process.

Textbooks / References
Gilbert Strang, "Differential Equations and Linear Algebra Wellesley”, Cambridge press, 2018.

Gilbert Strang, Wellesley, "Linear Algebra and learning from data”, Cambridge press, 2019.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Stephen Boyd and Lieven Vandenberghe, "Convex Optimization”, Cambridge University Press, 2018.
Stephen Boyd and, Lieven Vandenberghe, "Introduction to Applied Linear Algebra – Vectors, Matrices, and
Least Squares", Cambridge University Press, 2018.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 3) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE201 FUNDAMENTALS OF AI L-T-P-C: 2- 0- 3- 3

Course Objectives

 To introduce classical AI and rational intelligent agents.

 To introduce techniques for problem solving by search and adversarial games.
 To introduce constraints, logic, and inference techniques
 To introduce planning, acting, and multi-agent systems.
 To introduce knowledge-representation and reasoning.

Course Outcomes
After completing this course, students will be able to

CO1: Analyse different elements of an AI system.

CO2: Apply elementary principles of AI for problem solving and search

CO3: Apply constraints and logic for intelligent systems

CO4: Apply knowledge representation and reasoning for defining intelligent systems,

CO-PO Mapping

PO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO

CO1 3 2 2 2 3 2 2 2 2 2 - 2 3 2 3

CO2 2 2 2 2 3 - - - 2 2 - 2 3 2 3

CO3 2 2 2 2 3 - - - 2 2 - 2 3 2 3

CO4 3 2 2 2 3 - - - 2 2 2 2 3 2 3

Syllabus:

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Unit 1
History and Foundations of AI, Rational Intelligent Agents, Agents and Environments, Nature of Environments,
Structure of Agents.

Unit 2
Problem Solving by Search: Uninformed and Informed Search Strategies, Heuristic Functions; Adversarial
Search: Games, Optimal Decisions in Games, Alpha-Beta Pruning

Unit 3
Constraint Satisfaction Problems, Inference in CSPs, Backtracking Search; Knowledge-Based Agents,
Propositional and First-Order Logic, Resolution Theorem Proving, Unification Forward and Backward Chaining

Unit 4
Classical Planning: Algorithms for Planning, Planning Graphs, Hierarchical Planning, Planning and Acting in
Nondeterministic Domain, Multi-Agent Planning; Knowledge Representation: Ontological Engineering,
Categories and Objects, Events, Reasoning with Default Information.

Textbooks/ References:
Russell, Stuart Jonathan, Norvig, Peter, Davis, Ernest. Artificial Intelligence: A Modern Approach. United
Kingdom: Pearson, 2010.

Deepak Khemani. A First Course in Artificial Intelligence. McGraw Hill Education (India), 2013.

Denis Rothman. Artificial Intelligence by Example, Packt, 2018.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE202 OPERATING SYSTEMS L-T-P-C: 2- 0- 3- 3

Course Objectives

 This course gives an insight to the important problems in operating system design and implementation.
 This course helps the students to understand the operating system responsibilities like sharing
resources, files, memory and process scheduling.
 This course covers the major components of most operating systems and the trade-offs between
performance and functionality in the design and implementation of an operating system.
 In this course, emphasis will be given to three major OS subsystems: process management, memory
management, and file systems; and on operating system support for distributed systems.

Course Outcomes
After completing this course, the students will be able to

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO1: Apply system calls to implement basic OS functionalities.
CO2: Apply the algorithms for resource management and scheduling.
CO3: Apply semaphores and monitors for synchronization problems.
CO4: Implement memory management schemes.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 2 1 2 3 1 - - - 3 3 - 3 3 3 -
CO2 2 1 2 3 1 - - - 3 3 - 3 3 2 -
CO3 2 1 2 3 1 - - - 3 3 - 3 3 3 -
CO4 1 - - 1 3 - - - 3 3 - 3 - 1 -

Syllabus

Unit 1
Operating systems, structure, operating systems services, system calls. Process and Processor management:
Process concepts, process scheduling and algorithms, threads, multithreading. CPU scheduling and scheduling
algorithms

Unit 2
Process synchronization, critical sections, Deadlock: Shared resources, resource allocation and scheduling,
resource graph models, deadlock detection, deadlock avoidance, deadlock prevention algorithms, mutual
exclusion, semaphores, monitors, wait and signal procedures. Memory management: contiguous memory
allocation, virtual memory, paging, page table structure, demand paging, page replacement policies, thrashing,
segmentation, case study.

Unit 3
Disk scheduling algorithms and policies, File management: file concept, types and structures, directory structure,
Case study on Unix (about process management, Thread management and Kernel) and Mobile OS – iOS and
Android – Architecture and SDK Framework, Media Layer, Services Layer, Core OS Layer, File System)

Textbooks/References

Silberschatz and Galvin, “Operating System Concepts”, 9th Edition, Wiley India, 2009.
Tannenbaum A S, “Modern Operating Systems”, Prentice Hall India, 2003.
W. Stallings, “Operating Systems: Internals and design Principles”, Pearson Ed., LPE, 6th Ed., 2009
M.J. Bach, “Design of Unix Operating system”, Prentice Hall, 1986

Evaluation Plan

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE203 DATA STRUCTURES & ALGORITHMS 2 L-T-P-C: 2- 0- 3- 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Course Objectives

 This course helps students to implement and understand space and time optimizing structures and learn
their behaviours
 This course helps students to comprehend multidimensionality in memory structures
 This course helps students to understand the geometric organization of data
 This course provides an overview of space-building and immutability in functional data structure
 This course gives an introduction to graphical structures and use them in solving problems

Course Outcomes
After completing this course, the students will be able to

CO1: Design suitable data structures for problem-solving.

CO2: Use appropriate data structures for problem-solving scenarios.
CO3: Apply the interoperability of data structures to solve problems.
CO4: Visualize multidimensional geometry of data structure and concurrency.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 2 1 2 3 1 - - - 3 3 - 3 3 3 -
CO2 2 1 2 3 1 - - - 3 3 - 3 3 2 -
CO3 2 1 2 3 1 - - - 3 3 - 3 3 3 -
CO4 1 - - 1 3 - - - 3 3 - 3 - 1 -

Syllabus

Unit 1
Graphs- Representations of graphs, Adjacency and Incidence matrices, Adjacency List, Dynamic Graphs and
persistence - Sparse Matrices- Key Value and Structural implementations, Scalability and data driven parallelism,
Block and band matrices. Generalized Matrix and Vector interface. Standard implementations in Numpy (Python)
and NDArray (Java) - Temporal manipulation and persistence

Unit 2
Functional data structures, ConsList, immutable Set, Immutable Maps, Sorting immutable linear structures
(functional sort). Map and Reduce Operations on Sequences

Unit 3
Retroactive structures and operations – Geometric structures- Point location and sweeping, Orthogonal Range
searches and fractional cascading in 2D and 3D. -Higher data structures - Tries and inverted Tries- Radix Sort,
Higher Hash functions, SHA256, Chaining of Hash Lists (Blockchain) and change detection, Merkel trees-
Distributed bitwise representations and Fusion trees - large string structures (Google and DNA problems)

Textbooks/References
Mehlhorn, Kurt, Peter Sanders, and Peter Sanders. Algorithms and data structures: The basic toolbox.
Vol. 55. Berlin: Springer, 2008.
Bhim P Upadhyaya, Data Structures and Algorithms with Scala. Springer International Publishing,
2019.
Aho, Alfred V. "Data Structures and Algorithms, Addison-Wesley." Reading, Mass. (1983).

Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2009. Introduction to
Algorithms, Third Edition (3rd ed.). The MIT Press

Evaluation Plan

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE204 INTRODUCTION TO COMPUTER NETWORKS L-T-P-C: 2-0-3- 3

Course Objectives

 This course helps students to understand the fundamental networking concepts and standards.
 This course helps students to understand the function of TCP/IP layers and the protocols involved.
 This course helps students to understand the configuration of different networks and routing using
simulator/emulator.
 This course provides an overview of internet of things, its various applications, and their
implementation using simulator/emulator/Raspberry-PI.
 This course gives an introduction to the concepts of software defined networks and its applications.

Course Outcomes
After completing this course, the students will be able to

CO1: Analyse the requirements for a given organizational structure to select the most appropriate networking
architecture and technologies.
CO2: Analyse the working of protocols in the internet protocol stack for network applications.
CO3: Configure a router using simulator/emulator.
CO4: Implement IoT applications using simulator/emulator/Raspberry Pi.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 2 1 2 3 - - - - - - 2 - 2 -
CO2 3 3 2 - 3 2 - - - - - 2 - 2 -
CO3 3 3 2 2 3 - 2 2 2 2 - 3 - 2 -
CO4 3 3 3 3 3 3 3 1 3 2 - 2 - 2 1

Syllabus

Unit 1
Basic concepts of computer networks, Internet-The Network Edge, the Network Core, Network Topology, Types
of Networks. Circuit switched networks vs packet switched network, Delay, Loss, and Throughput in Packet
Switched Networks. OSI layer stack, Introduction to applications in networking, protocols in the context of the
Internet protocol stack. Internet standards and organization

Unit 2
Application Layer – Protocols in Web and Email applications, Peer-to-Peer Applications. Transport Layer –
connection-oriented and connectionless service, protocols, and socket programming. Network Layer – Internet
Protocol, Host Addressing for subnets, Routing and Forwarding principles, Router configuration. Configuration
and implementation of local area networks and intranets in simulator or emulator. Data link and Physical layer
concepts for wired and wireless network

Unit 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Internet of Things – Components like controllers, services, Fog and cloud computing, Applications. Configuration
and implementation of IoT applications in simulator or emulator or real time hardware devices like Raspberry Pi.
Introduction to Software Define Networks, Mininet and OpenFlow.

Textbooks/References
Kurose, James F. Computer networking: A top-down approach featuring the internet, 3/E. Pearson Education
India, 2005.
Behrouz A Forouzan, and G. Hill. Data Communications and Networking, by Behrouz, 2006.
Rick Golden, Raspberry Pi Networking Cookbook – Second Edition, 2017

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE205 INTRODUCTION TO PYTHON L-T-P-C: 1- 0- 3- 2

Course Objectives

 To acquire programming skills in core Python.

 To understand how to write functions and pass arguments in Python.
 To develop fundamental understanding of how to build and package Python modules for reusability.
 To develop program in python to read and write files.

Course Outcomes

After completing this course, students will be able to

CO1: Solve problems using Python conditionals and loops.

CO2: Apply Python functions and function calls to solve problems.
CO3: Apply Python data structures to represent complex data.
CO4: Develop Python Packages for reusability.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

CO

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO1 3 3 2 2 3 - - - 3 2 3 3 1 1 1

CO2 3 3 3 3 3 - - - 3 2 3 3 1 1 1

CO3 3 2 3 3 3 - - - 3 2 3 3 1 1 1

CO4 3 2 3 3 3 - - - 3 2 1 3 1 1 1

Syllabus
Introduction to Python Control Statements-List, Ranges & Tuples in Python-Python Dictionaries and Sets-
Input and Output in Python-Python built in function-Python Object Oriented-Exceptions-Python Regular
Expressions-Python Multithreaded Programming-Using Databases in Python-Regular Expression -Thread
Essentials-Web Scraping in Python-Data Science Using Python-Graphical User Interface-Django Web
Framework in Python Interface of python with an SQL database-Connecting SQL with Python-Performing Insert,
Update, Delete Queries using Cursor-NumPy-Pandas and data frame operations on Toyota Corolla dataset-Data
visualization; matplotlib, seaborn libraries-Python Libraries

Textbooks
Allen B. Downey, “Think Python: How to Think like a Computer Scientist”, 2nd Edition, O’Reilly Publishers,
2016.

References
Paul Deitel and Harvey Deitel, “Python for Programmers”, Pearson Education, 1st Edition, 2021.
Eric Matthes, “Python Crash Course, A Hands – on Project Based Introduction to Programming”, 2nd Edition,
No Starch Press, 2019.
https://www.python.org/,numpy.org
Martin C. Brown, “Python: The Complete Reference”, 4th Edition, Mc-Graw Hill, 2018.
David Beazley, Brian Jones., “Python Cookbook”, Third Edition, Orelly Publication, 2013, ISBN 978-
1449340377

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 1) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22BIO201 INTELLIGENCE OF BIOLOGICAL SYSTEMS 1 L-T-P-C: 2- 0- 0- 2

Course Objectives

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  Understanding the basic concepts about biomolecules, cell division, central dogma of the cell,
mutations and evolutionary patterns
 Knowledge about biological databases, bacterial genomes and its hidden message.
 Understanding the mechanism of DNA methylation in circadian rhythm
 Application of statistical methods in sequence analysis and motif finding

Course Outcomes

After completing this course, the students will be able to

CO1: Apply the cellular structure and biophysical process for creating engineered models.

CO2: Incorporate the application of molecular mechanisms to build advanced computational pipelines.
CO3: Apply statistical estimation and test of significance techniques for Motifs and to learn python for using
biological databases.

CO4: Apply chaos model to represent DNA Sequences.

CO-PO Mapping

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

CO 1 1 1 1 1 3 2 2 3 2 3 2 1
CO 2 1 1 1 1 3 2 2 3 2 3 1 1
CO 3 1 3 2 2 3 2 2 3 2 3 2 1
CO4 2 1 2 3 - 1 1 3 2 2 1 1

Syllabus

Unit 1

Classification of Biomolecules Cell division: Mitosis and Meiosis; Central Dogma of the cell: Replication,
Transcription, Translation, Protein Synthesis; Genetic Variants of Evolutionary Patterns: Mutations and
Polymorphisms.

Unit 2

Introduction to biological databases-Hidden messages in the genome – Finding Replication Origins - Frequent
words in Vibrio cholera – Encodings in DNA to maintain circadian rhythm Basics of Probability-Probability
Distributions.

Unit 3

Statistics-Statistical Estimation and Inference of Sequence Analysis in Matlab -and Python – Simple values,
names, expression, module, collection, sequences, mapping and expression feature. Hunting for
Regulatory Motifs - Scoring Motifs - Motif Search – Greedy & Randomized Motif Search – Gibbs Sampling-
Chaos representation - DNA sequences comparison of related viruses.

Textbooks/References

Gabi Nindl Waite, Lee R Waite, Applied Cell and Molecular Biology for Engineers, McGraw Hill Publishers,
2007.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Phillip Compeau & Pavel Pevzner, Bioinformatics algorithm, An active learning Approach Vol.1. and Vol. 2 ,
2015.

George M. Malascinski, Freifelder’s Essentials of Molecular Biology, 4th Edition, Jones and Bartlett Student
Edition, 2015.

DM.Vasudevan, Sreekumari S, Kannan Vaidyanathan, Textbook of Biochemistry for Medical Students (As Per
Revised MCI Curriculum),9th Edition, Jaypee Publishers, 2019.

David Nelson, Michael M Cox, LeningerPrinciples of Biochemistry, 8th Edition, Macmillan, 2021.

Evaluation pattern
Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 1) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

AMRITA VALUES PROGRAMME

Amrita University's Amrita Values Programme (AVP) is a new initiative to give exposure to students about
richness and beauty of Indian way of life. India is a country where history, culture, art, aesthetics, cuisine and
nature exhibit more diversity than nearly anywhere else in the world.

Amrita Values Programmes emphasize on making students familiar with the rich tapestry of Indian life, culture,
arts, science and heritage which has historically drawn people from all over the world.

Students shall have to register for any two of the following courses, one each in the third and the fourth semesters,
which may be offered by the respective school during the concerned semester.

Course Outcomes

CO1: Understanding the impact of itihasas on Indian civilization with a special reference to the Adiparva of
Mahabharata
CO2: Enabling students to importance offightingadharma for the welfare of the society through Sabha and
Vanaparva.
CO3: Understanding the nuances of dharma through the contrast between noble and ignoble characters of the
epic as depicted in the Vana, Virata, Udyoga and Bhishmaparvas.
CO4: Getting the deeper understanding of the Yuddha Dharma through the subsequent Parvas viz., Drona, Karna,
Shalya, SauptikaParvas.
CO5: Making the students appreciative of spiritual instruction on the ultimate triumph of dharma through the
presentations of the important episodes of the MB with special light on Shanti, Anushasana,
Ashwamedhika, Ashramavasika, Mausala, Mahaprasthanika and SwargarohanaParvas.

CO-PO Mapping

PO/PSO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO1 - - - - - 2 2 3 3 3 - 3 - -
CO2 - - - - - 3 3 3 3 2 - 3 - -
CO3 - - - - - 3 2 3 3 3 - 3 - -
CO4 - - - - - 3 - 3 3 3 - 3 - -
CO5 - - - - - 3 - 3 3 2 - 3 - -

Courses offered under the framework of Amrita Values Programmes I and II

Message from Amma’s Life for the Modern World

Amma’s messages can be put to action in our life through pragmatism and attuning of our thought process in a
positive and creative manner. Every single word Amma speaks and the guidance received in on matters which
we consider as trivial are rich in content and touches the very inner being of our personality. Life gets enriched
by Amma’s guidance and She teaches us the art of exemplary life skills where we become witness to all the
happenings around us still keeping the balance of the mind.

Lessons from the Ramayana

Introduction to Ramayana, the first Epic in the world – Influence of Ramayana on Indian values and culture –
Storyline of Ramayana – Study of leading characters in Ramayana – Influence of Ramayana outside India –
Relevance of Ramayana for modern times.

Lessons from the Mahabharata

Introduction to Mahabharata, the largest Epic in the world – Influence of Mahabharata on Indian values and culture
– Storyline of Mahabharata – Study of leading characters in Mahabharata – Kurukshetra War and its significance
- Relevance of Mahabharata for modern times.

Lessons from the Upanishads

Introduction to the Upanishads: Sruti versus Smrti - Overview of the four Vedas and the ten Principal Upanishads
- The central problems of the Upanishads – The Upanishads and Indian Culture – Relevance of Upanishads for
modern times – A few Upanishad Personalities: Nachiketas, SatyakamaJabala, Aruni, Shvetaketu.

Message of the Bhagavad Gita

Introduction to Bhagavad Gita – Brief storyline of Mahabharata - Context of Kurukshetra War – The anguish of
Arjuna – Counsel by Sri. Krishna – Key teachings of the Bhagavad Gita – Karma Yoga, Jnana Yoga and Bhakti
Yoga - Theory of Karma and Reincarnation – Concept of Dharma – Concept of Avatar - Relevance of
Mahabharata for modern times.

Life and Message of Swami Vivekananda

Brief Sketch of Swami Vivekananda’s Life – Meeting with Guru – Disciplining of Narendra - Travel across India
- Inspiring Life incidents – Address at the Parliament of Religions – Travel in United States and Europe – Return
and reception India – Message from Swamiji’s life.

Life and Teachings of Spiritual Masters India

Sri Rama, Sri Krishna, Sri Buddha, AdiShankaracharya, Sri Ramakrishna Paramahamsa, Swami Vivekananda,
Sri RamanaMaharshi, Mata Amritanandamayi Devi.

Insights into Indian Arts and Literature

The aim of this course is to present the rich literature and culture of Ancient India and help students appreciate
their deep influence on Indian Life - Vedic culture, primary source of Indian Culture – Brief introduction and
appreciation of a few of the art forms of India - Arts, Music, Dance, Theatre.

Yoga and Meditation

The objective of the course is to provide practical training in YOGA ASANAS with a sound theoretical base
and theory classes on selected verses of Patanjali’s Yoga Sutra and Ashtanga Yoga. The coverage also includes
the effect of yoga on integrated personality development.

Kerala Mural Art and Painting

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Mural painting is an offshoot of the devotional tradition of Kerala. A mural is any piece of artwork painted or
applied directly on a wall, ceiling or other large permanent surface. In the contemporary scenario Mural painting
is not restricted to the permanent structures and are being done even on canvas. Kerala mural paintings are the
frescos depicting mythology and legends, which are drawn on the walls of temples and churches in South India,
principally in Kerala. Ancient temples, churches and places in Kerala, South India, display an abounding tradition
of mural paintings mostly dating back between the 9th to 12th centuries when this form of art enjoyed Royal
patronage. Learning Mural painting through the theory and practice workshop is the objective of this course.

Course on Organic Farming and Sustainability

Organic farming is emerging as an important segment of human sustainability and healthy life. Haritamritam’ is
an attempt to empower the youth with basic skills in tradition of organic farming and to revive the culture of
growing vegetables that one consumes, without using chemicals and pesticides. Growth of Agriculture through
such positive initiatives will go a long way in nation development. In Amma’swords “it is a big step in restoring
the lost harmony of nature“.

Benefits of Indian Medicinal Systems

Indian medicinal systems are one of the most ancient in the world. Even today society continues to derive
enormous benefits from the wealth of knowledge in Ayurveda of which is recognised as a viable and sustainable
medicinal tradition. This course will expose students to the fundamental principles and philosophy of Ayurveda
and other Indian medicinal traditions.

Traditional Fine Arts of India

India is home to one of the most diverse Art forms world over. The underlying philosophy of Indian life is ‘Únity
in Diversity” and it has led to the most diverse expressions of culture in India. Most art forms of India are an
expression of devotion by the devotee towards the Lord and its influence in Indian life is very pervasive. This
course will introduce students to the deeper philosophical basis of Indian Art forms and attempt to provide a
practical demonstration of the continuing relevance of the Art.

Science of Worship in India

Indian mode of worship is unique among the world civilisations. Nowhere in the world has the philosophical idea
of reverence and worshipfulness for everything in this universe found universal acceptance as it in India. Indian
religious life even today is a practical demonstration of the potential for realisation of this profound truth. To see
the all-pervading consciousness in everything, including animate and inanimate, and constituting society to realise
this truth can be seen as the epitome of civilizational excellence. This course will discuss the principles and
rationale behind different modes of worship prevalent in India.

TEXT BOOKS/REFERENCES:
1. Rajagopalachari. C, The Ramayana

Valmiki, The Ramayana, Gita Press

SEMESTER IV

22MAT230 MATHEMATICS FOR COMPUTING 4 L-T-P-C: 2- 1- 3- 4

Course Objectives

 The course will lay down the basic concepts and techniques of linear algebra, calculus and basic
probability theory needed for subsequent study.
 It will explore the concepts initially through computational experiments and then try to understand the
concepts/theory behind it.
 At the same time, it will provide an appreciation of the wide application of these disciplines within the
scientific field.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  Another goal of the course is to provide connection between the concepts of linear algebra, differential
equation and probability theory.

Course Outcomes

After completing this course student will be able to,

CO1: Demonstrate the techniques of linear algebra needed for AI.

CO2: Apply the tools and techniques of optimization to analyze physical systems.

CO3: Apply the principles of statistics to perform data analysis.

CO4: Apply modern computational tools and techniques for solving advanced problems in linear algebra,
optimization, and statistics needed for AI.

CO-PO Mapping

PO/ PO PO PO PO PSO PSO PSO

PO1 PO4 PO5 PO6 PO9 PO10 PO11 PO12
PSO 2 3 7 8 1 2 3
CO1 3 3 2 3 3 --- --- --- 3 2 --- 3 3 --- 3
CO2 3 3 2 3 3 --- --- --- 3 2 --- 3 3 --- 3
CO3 3 3 2 3 3 --- --- --- 3 2 --- 3 3 --- 3
CO4 3 3 2 3 3 --- --- --- 3 2 --- 3 3 --- 3

Syllabus

Unit 1

Linear Algebra-4
Special Matrices: Fourier Transform, discrete and Continuous, Shift matrices and Circulant matrices, The
Kronecker product, Toeplitz matrices and shift invariant filters, Graphs and Laplacians and Kirchhoff’s laws,
Clustering by spectral methods and K-means, Completing rank one matrices, The Orthogonal Procrustes Problem,
Distance matrices.

Unit 2

Calculus-4
Optimization methods for sparsity: Split algorithm for L2+ L1, Split algorithm for L1 optimization, Augmented
Lagrangian, ADMM, ADMM for LP and QP, Matrix splitting and Proximal algorithms, Compressed sensing, and
Matrix Completion.

Optimization methods for Neural Networks: Gradient Descent, Stochastic gradient descent, and ADAM (adaptive
methods), Loss function and learning function.

Unit 3
Probability and statistics - 4

Basics of statistical estimation theory and testing of hypothesis.

Textbooks / References
Gilbert Strang, Linear Algebra and learning from data, Wellesley, Cambridge press, 2019.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Bradley Efron , Trevor Hastie, Computer Age Statistical Inference, Algorithms, Evidence and Data Science.
Stephen Boyd, Lieven Vandenberghe, Convex Optimization, Cambridge University Press, 2018.

Stephen Boyd , Lieven Vandenberghe, Introduction to Applied Linear Algebra – Vectors, Matrices, and Least
Squares, Cambridge University Press, 2018.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 3) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE211 INTRODUCTION TO COMMUNICATION & IoT L-T-P-C: 2- 0- 3- 3

Course Objective

1. The course will expose the students to basics of communication systems

2. This course will realize communication experiments using various software defined radio systems.
3. The course provides an understanding on the basics of Internet of Things, architecture and its protocols.

Course Outcomes

After completing this course student will be able to,

CO1: Familiarise the basic concepts of communication systems
CO2: Realise various communication systems using software defined radio systems.
CO3: Familiarise basics of Internet of Things and its architecture
CO4: Interface I/O devices and communication modules.

CO-PO Mapping

PO/ PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO PSO PSO
PSO 1 2 3
CO1 3 2 2 2 3 - - - 3 2 2 3 - 2 1
CO2 3 2 3 3 3 - - - 3 2 2 3 1 2 -
CO3 3 2 2 2 3 - - - 3 2 2 3 - 2 -
CO4 3 2 3 3 3 - - - 3 2 3 3 - 2 1

Syllabus

Unit 1

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Introduction to signals - types and characteristics; Introduction to communication systems: Wired and Wireless
communication systems – Modulations: AM, FM, PM - Digital modulation and de-modulation techniques: ASK,
FSK, BPSK and QAM – OFDM - MATLAB and GNURADIO for Communication system experiments.

Unit 2

Introduction to IoT - Architectural overview- Design principles- IoT Applications- M2M and IoT Technology
Fundamentals.

Unit 3

Elements of IoT: Hardware components, Communication Technologies, Sensing, Actuation, I/O interfaces
Software Components- Programming APIs for communication protocols-MQTT, Zigbee, Bluetooth, CoAP, UDP,
TCP.

Text books/ Reference Books:

Boylestad, Robert L., and Louis Nashelsky. Electronic devices and circuit theory. Prentice Hall, 2012.
Qasim Chaudhari, Wireless Communications from the Ground Up: Fundamentals of Digital Communication
Systems, Createspace Independent Publishers, 2016
Vijay Madisetti, Arshdeep Bahga, Internet of Things, “A hands on Approach”, University Press
Herrero, Rolando. Fundamentals of IoT Communication Technologies. Springer International Publishing, 2022.
Dr SRN Reddy, Rachit Thukral and Manasi Mishra,” Introduction to Internet of Things”: A practical Approach”
ETI Labs
The Internet of Things: Applications and Protocols, Wiley publications. Author(s): Oliver Hersent, David
Boswarthick, Omar Elloumi

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE212 DESIGN AND ANALYSIS OF ALGORITHMS L-T-P-C: 2- 0- 3- 3

Course Objectives

 This course helps students to impart various design techniques for formulation of algorithm.
 This course helps students to understand basic categories of algorithms.
 This course helps students to understand and apply analysis of space and time complexity of algorithms
and understand concept of growth rate.
 This course helps students to deliver standard notations and representations of algorithmic complexity
and known complexities.
 This course helps students to comprehend basic complexity classes.
 This course helps students to acquaint with will know tractable and intractable problems and map
solutions to it.

Course Outcomes
After completing this course, the students will be able to

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO1: Develop skills for analyzing algorithmic strategies.
CO2: Apply appropriate algorithmic technique for a given problem.
CO3: Implement standard algorithms on arrays, strings, trees and graph.
CO4: Analyse the nature of known classes of tractable or intractable problem.

CO-PO Mapping
PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 3 3 3 3 3 1 - 3 3 2 3 3 1 -
CO2 3 3 3 2 3 2 - - 3 3 2 3 3 2 -
CO3 3 3 3 3 2 1 - - 3 3 3 3 3 3 -
CO4 3 3 3 3 2 1 - - 3 3 3 3 2 3 -

Syllabus

Unit 1
Notion of an Algorithm – Fundamentals of Algorithmic Problem Solving – Important Problem Types –
Fundamentals of the Analysis of Algorithmic Efficiency –Asymptotic Notations and growth rate- Empirical
analysis – Recursive and non-Recursive Templates. Brute Force: Exhaustive Search and String Matching, Divide
and Conquer Methodology: Binary Search – Merge sort – Quick sort – Heap Sort – Multiplication of Large
Integers.

Unit 2
Dynamic programming: Principle of optimality – Coin changing problem, Computing a Binomial Coefficient –
Floyd‘s algorithm – Multi stage graph – Optimal Binary Search Trees – Knapsack Problem and Memory functions.
Greedy Technique: Container loading problem – Huffman Trees. Iterative methods: The Simplex Method – The
Maximum-Flow Problem – Maximum Matching in Bipartite Graphs, Stable marriage Problem, Measuring
Limitations: Lower – Bound Arguments – P, NP, NP- Complete and NP Hard Problems.

Unit 3
Backtracking – n-Queen problem – Hamiltonian Circuit Problem – Subset Sum Problem, Branch and Bound –
LIFO Search and FIFO search – Assignment problem – Knapsack Problem – Travelling Salesman
Problem, Approximation Algorithms for NP-Hard Problems – Travelling Salesman problem – Knapsack problem
revisited.

Textbooks/References
Jeffrey McConnell, Analysis of algorithms. Jones & Bartlett Publishers, 2nd Revised edition, 2007.
Anany Levitin, Introduction to the Design and Analysis of Algorithms, Third Edition, Pearson Education, 2012.
Harsh Bhasin, Algorithms Design and Analysis, Oxford university press, 2016

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE213 MACHINE LEARNING L-T-P-C: 2- 0- 3- 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Course Objectives

 This course dives into the basics of Machine learning.

 This course will enable the students to work with various types of data and its pre-processing
techniques.
 The students will learn about Supervised and Unsupervised Learning.
 The students will enrich themselves with hands-on experience to implement various machine learning
algorithms.

Course Outcomes

After completing this course, students will be able to

CO1: Apply pre-processing techniques to prepare the data for machine learning applications
CO2: Implement supervised machine learning algorithms for different datasets
CO3: Implement unsupervised machine learning algorithms for different datasets
CO4: Analyze the error to improve the performance of the machine learning models

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 - - 3 3 - - 1 3 3 1 3 3 3 -

CO2 3 2 3 - 3 - - 1 3 3 1 3 3 3 3

CO3 3 2 3 - 3 - - 1 3 3 1 3 3 3 3

CO4 3 3 - - 3 - - 1 3 3 1 3 - 3 -

Syllabus

Unit 1
Introduction to Machine Learning – Data and Features – Machine Learning Pipeline: Data Preprocessing:
Standardization, Normalization, Missing data problem, Data imbalance problem – Data visualization - Setting up
training, development and test sets – Cross validation – Problem of Overfitting, Bias vs Variance - Evaluation
measures – Different types of machine learning: Supervised learning, Unsupervised learning, Reinforcement
learning, Generative Learning and adversarial learning.

Unit 2
Supervised learning - Regression: Linear regression, logistic regression – Classification: K-Nearest Neighbor,
Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, Perceptron, Error analysis.

Unit 3
Unsupervised learning – Clustering: K-means, Hierarchical, Spectral, subspace clustering, Gaussian Mixture
Model, Hidden Markov Model, Parameter Estimation: MLE and Bayesian Estimate, Expectation Maximization,
Dimensionality Reduction Techniques, Principal component analysis, Linear Discriminant Analysis.

Unit 4
Introduction to Neural Networks, Reinforcement learning and generative learning.

Text Books
Andrew Ng, Machine learning yearning, URL: http://www. mlyearning. org/(96) 139 (2017).
Kevin P. Murphey. Machine Learning, a probabilistic perspective. The MIT Press Cambridge, Massachusetts,
2012.
Christopher M Bishop. Pattern Recognition and Machine Learning. Springer 2010

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

References
Richard O. Duda, Peter E. Hart, David G. Stork. Pattern Classification. Wiley, Second Edition;2007
Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE214 INTRODUCTION TO AI ROBOTICS L-T-P-C: 2- 0- 3- 3

Course Objectives

 To provide an introductory understanding of robots and its components.

 To introduce different paradigms used in AI robotics.
 To introduce the mathematical concepts needed for understanding basic robotic system operation.
 To introduce kinematics and its application in robotic manipulators.
Course Outcomes

After completing this course student will be able to,

CO1: Analyse a robotic system using different paradigms of AI robotics.

CO2: Apply mathematical concepts to represent the position and orientation of robotic systems.
CO3: Perform the forward and inverse kinematics of canonical robotic systems.
CO4: Simulate robotic systems using state-of-the-art computational platforms.

CO-PO Mapping

PO PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO

1 2 3 4 5 6 7 8 9 0 1 2 1 2 3
CO

CO 3 1 3 2 1 2 2 2 3 3 3 3 3 2 3
1
CO 3 3 2 2 3 - - - 3 3 - 3 3 1 3
2
CO 3 3 2 2 3 - - - 3 3 - 3 3 1 3
3
CO 3 3 3 3 3 - - - 3 3 3 3 3 3 3
4

Syllabus

Unit 1

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Introduction to robots – Brief History – Types of robots – Teleoperation.

Unit 2
Attributes of the hierarchal paradigm - Attributes of the reactive paradigm – Biological foundations of the reactive
paradigm – Common sensing techniques for reactive robots – Attributes of hybrid paradigm.

Unit 3
Mathematical representation of robots – Position and orientation of rigid bodies – Rotation and Orientation –
Quaternions and other rotation representations - Transformation Matrix

Unit 4

D-H parameters – Forward and inverse kinematics of robot manipulators.

Text Book /Reference Books

'Robotics, Vision & Control’, P. Corke, 2nd edition, Springer, 2011
‘Robot Modeling and Control’, M.W. Spong, S. Hutchinson and M. Vidyasagar, Wiley, 2006
‘Robotics: Fundamental Concepts & Analysis’, A. Ghosal, Oxford University Press, Ninth Edition, 2006
‘Introduction to Robotics’, T. Bajd, M. Mihelj and M. Munih, Springer Briefs in Applied Sciences and Technology,
2013
‘Introduction to AI Robotics’, Robin Murphy, MIT Press, 2000

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22BIO211 INTELLIGENCE OF BIOLOGICAL SYSTEMS 2 L-T-P-C: 2- 0- 3- 3

Course Objectives

 Application of statistics to interpret biological sequence analysis.

 Application of programming to compare biological sequences.
 Evaluation of algorithms in antibiotic sequencing.
 Evaluation of statistical models in Bioinformatics.

Course Outcomes

After completing this course, the students will be able to,

CO1: Apply Dynamic Programming in Sequence Alignment.

CO2: Apply Brute Force Method in Sequence Analysis.
CO3: Apply Graph Theory in Genome Assembly.
CO4: Apply Deep Learning in Bioinformatics.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO-PO Mapping

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

CO 1 1 1 1 1 3 2 3 2 3 2 1
CO 2 1 1 1 1 3 2 3 2 3 1 1
CO 3 1 3 2 2 3 2 3 2 3 2 1
CO4 2 1 2 3 1 3 2 2 1 1

Syllabus

Unit-1

Antibiotics Sequencing – Shattering into pieces – Brute force algorithm for Cyclopeptide Sequencing –
Comparison of biological sequences – Cracking the Non-Ribosomal Code – Introduction to Sequence Alignment
– Introduction to Dynamic Programming, building a Manhattan-like graph - Mass Spectrometry- From 20 to more
than 100 Amino Acids

Unit-2

Introduction - Assembling Genomes using Graph algorithms - String reconstruction problem – String
reconstruction as a walk in the overlap graph – Gluing nodes – de Bruijn graphs – the seven bridges of Konigsberg
Euler’s theorem– Eulerian Cycle – Assembling genomes from read-pairs –Introduction to deep-learning in
bioinformatics.

Textbooks/References

Jin Xiong , Essential Bioinformatics , Cambridge University Press, 2006.

Gerald Karp, Chapter 15- Cell Signaling and Signal Transduction: Communication Between Cells, In Cell and
Molecular Biology: Concepts and Experiments, 7e, Wiley, 2013.

Phillip Compeau & Pavel Pevzner, Bioinformatics algorithm, An active learning Approach Vol.1. and Vol. 2 ,
2015.

Karthik Raman, an Introduction to Computational Systems Biology (Systems Level Modeling of Cellular
Networks), CRC Press, 2021.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 AMRITA VALUES PROGRAMME L-T-P-C: 1- 0-0 -1

Amrita University's Amrita Values Programme (AVP) is a new initiative to give exposure to students about
richness and beauty of Indian way of life. India is a country where history, culture, art, aesthetics, cuisine and
nature exhibit more diversity than nearly anywhere else in the world.

Amrita Values Programmes emphasize on making students familiar with the rich tapestry of Indian life, culture,
arts, science and heritage which has historically drawn people from all over the world.

Students shall have to register for any two of the following courses, one each in the third and the fourth semesters,
which may be offered by the respective school during the concerned semester.

Course Outcomes

CO1: Understanding the impact of itihasas on Indian civilization with a special reference to the Adiparva of
Mahabharata
CO2: Enabling students to importance offightingadharma for the welfare of the society through Sabha and
Vanaparva.
CO3: Understanding the nuances of dharma through the contrast between noble and ignoble characters of the
epic as depicted in the Vana, Virata, Udyoga and Bhishmaparvas.
CO4: Getting the deeper understanding of the Yuddha Dharma through the subsequent Parvas viz., Drona, Karna,
Shalya, SauptikaParvas.
CO5: Making the students appreciative of spiritual instruction on the ultimate triumph of dharma through the
presentations of the important episodes of the MB with special light on Shanti, Anushasana,
Ashwamedhika, Ashramavasika, Mausala, Mahaprasthanika and SwargarohanaParvas.

CO-PO Mapping

PO/PSO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 - - - - - 2 2 3 3 3 - 3 - -
CO2 - - - - - 3 3 3 3 2 - 3 - -
CO3 - - - - - 3 2 3 3 3 - 3 - -
CO4 - - - - - 3 - 3 3 3 - 3 - -
CO5 - - - - - 3 - 3 3 2 - 3 - -

Courses offered under the framework of Amrita Values Programmes I and II

Message from Amma’s Life for the Modern World

Amma’s messages can be put to action in our life through pragmatism and attuning of our thought process in a
positive and creative manner. Every single word Amma speaks and the guidance received in on matters which
we consider as trivial are rich in content and touches the very inner being of our personality. Life gets enriched
by Amma’s guidance and She teaches us the art of exemplary life skills where we become witness to all the
happenings around us still keeping the balance of the mind.

Lessons from the Ramayana

Introduction to Ramayana, the first Epic in the world – Influence of Ramayana on Indian values and culture –
Storyline of Ramayana – Study of leading characters in Ramayana – Influence of Ramayana outside India –
Relevance of Ramayana for modern times.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Lessons from the Mahabharata
Introduction to Mahabharata, the largest Epic in the world – Influence of Mahabharata on Indian values and culture
– Storyline of Mahabharata – Study of leading characters in Mahabharata – Kurukshetra War and its significance
- Relevance of Mahabharata for modern times.

Lessons from the Upanishads

Introduction to the Upanishads: Sruti versus Smrti - Overview of the four Vedas and the ten Principal Upanishads
- The central problems of the Upanishads – The Upanishads and Indian Culture – Relevance of Upanishads for
modern times – A few Upanishad Personalities: Nachiketas, SatyakamaJabala, Aruni, Shvetaketu.

Message of the Bhagavad Gita

Introduction to Bhagavad Gita – Brief storyline of Mahabharata - Context of Kurukshetra War – The anguish of
Arjuna – Counsel by Sri. Krishna – Key teachings of the Bhagavad Gita – Karma Yoga, Jnana Yoga and Bhakti
Yoga - Theory of Karma and Reincarnation – Concept of Dharma – Concept of Avatar - Relevance of
Mahabharata for modern times.

Life and Message of Swami Vivekananda

Brief Sketch of Swami Vivekananda’s Life – Meeting with Guru – Disciplining of Narendra - Travel across India
- Inspiring Life incidents – Address at the Parliament of Religions – Travel in United States and Europe – Return
and reception India – Message from Swamiji’s life.

Life and Teachings of Spiritual Masters India

Sri Rama, Sri Krishna, Sri Buddha, AdiShankaracharya, Sri Ramakrishna Paramahamsa, Swami Vivekananda,
Sri RamanaMaharshi, Mata Amritanandamayi Devi.

Insights into Indian Arts and Literature

The aim of this course is to present the rich literature and culture of Ancient India and help students appreciate
their deep influence on Indian Life - Vedic culture, primary source of Indian Culture – Brief introduction and
appreciation of a few of the art forms of India - Arts, Music, Dance, Theatre.

Yoga and Meditation

The objective of the course is to provide practical training in YOGA ASANAS with a sound theoretical base and
theory classes on selected verses of Patanjali’s Yoga Sutra and Ashtanga Yoga. The coverage also includes the
effect of yoga on integrated personality development.

Kerala Mural Art and Painting

Mural painting is an offshoot of the devotional tradition of Kerala. A mural is any piece of artwork painted or
applied directly on a wall, ceiling or other large permanent surface. In the contemporary scenario Mural painting
is not restricted to the permanent structures and are being done even on canvas. Kerala mural paintings are the
frescos depicting mythology and legends, which are drawn on the walls of temples and churches in South India,
principally in Kerala. Ancient temples, churches and places in Kerala, South India, display an abounding tradition
of mural paintings mostly dating back between the 9th to 12th centuries when this form of art enjoyed Royal
patronage. Learning Mural painting through the theory and practice workshop is the objective of this course.

Course on Organic Farming and Sustainability

Organic farming is emerging as an important segment of human sustainability and healthy life. Haritamritam’ is
an attempt to empower the youth with basic skills in tradition of organic farming and to revive the culture of
growing vegetables that one consumes, without using chemicals and pesticides. Growth of Agriculture through
such positive initiatives will go a long way in nation development. In Amma’s words “it is a big step in restoring
the lost harmony of nature “.

Benefits of Indian Medicinal Systems

Indian medicinal systems are one of the most ancient in the world. Even today society continues to derive
enormous benefits from the wealth of knowledge in Ayurveda of which is recognised as a viable and sustainable
medicinal tradition. This course will expose students to the fundamental principles and philosophy of Ayurveda
and other Indian medicinal traditions.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Traditional Fine Arts of India
India is home to one of the most diverse Art forms world over. The underlying philosophy of Indian life is ‘Unity
in Diversity” and it has led to the most diverse expressions of culture in India. Most art forms of India are an
expression of devotion by the devotee towards the Lord and its influence in Indian life is very pervasive. This
course will introduce students to the deeper philosophical basis of Indian Art forms and attempt to provide a
practical demonstration of the continuing relevance of the Art.

Science of Worship in India

Indian mode of worship is unique among the world civilisations. Nowhere in the world has the philosophical idea
of reverence and worshipfulness for everything in this universe found universal acceptance as it in India. Indian
religious life even today is a practical demonstration of the potential for realisation of this profound truth. To see
the all-pervading consciousness in everything, including animate and inanimate, and constituting society to realise
this truth can be seen as the epitome of civilizational excellence. This course will discuss the principles and
rationale behind different modes of worship prevalent in India.

TEXT BOOKS/REFERENCES:
1. Rajagopalachari. C, The Ramayana

2. Valmiki, The Ramayana, Gita Press

19ENV300 ENVIRONMENTAL SCIENCE P/F

Course Objectives
 To study the nature and facts about environment
 To appreciate the importance of environment by assessing its impact on the human world
 To study the integrated themes and biodiversity, pollution control and waste management

Course Outcomes

CO1: Ability to understand aspects of nature and environment

CO2: Ability to analyse impact of environment on human world
CO3: Ability to comprehend pollution control and waste management

CO – PO Mapping

PO/PSO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 - - - - - 3 2 3 - - - - - -
CO2 - - - - - 3 2 3 - - - - - -
CO3 - - - - - 3 2 3 - - - - - -

Syllabus
Unit 1

Over view of the global environment crisis – Biogeochemical cycles – Climate change and related international
conventions and treaties and regulations – Ozone hole and related International conventions and treaties and
regulations – Overpopulation – energy crisis – Water crisis – ground water hydrogeology – surface water resource
development.

Unit 2
Ecology, biodiversity loss and related international conventions – treaties and regulations – Deforestation and
land degradation – food crisis – water pollution and related International and local conventions – treaties and

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

regulations – Sewage domestic and industrial and effluent treatment – air pollution and related international and
local conventions – treaties and regulations – Other pollution (land, thermal, noise).

Unit 3
Solid waste management (municipal, medical, e-waste, nuclear, household hazardous wastes) – environmental
management – environmental accounting – green business – eco-labelling – environmental impact assessment –
Constitutional – legal and regulatory provisions – sustainable development.

Text Book(s)
R. Rajagopalan,“Environmental Studies – From Crisis to Cure”, Oxford University Press, 2005, ISBN 0-19-
567393-X.

Reference(s)
G.T.Miller Jr., “Environmental Science”, 11th Edition, Cenage Learning Pvt. Ltd., 2008.
Benny Joseph, “Environmental Studies”, Tata McGraw-Hill Publishing company Limited, 2008.

Evaluation Pattern

Assessment Internal External

Online Test - 100
P/F

19SSK211 SOFT SKILLS I L-T-P-C: 1-0-3-2

Course Outcome

CO 1 - Soft Skills: At the end of the course, the students would have developed self-confidence and positive
attitude necessary to compete and challenge themselves. They would also be able to analyse and manage their
emotions to face real life situations.

CO 2 - Soft Skills: Soft Skills: At the end of the course, the students would hone their presentation skills by
understanding the nuances of content creation, effective delivery, use of appropriate body language and the art of
overcoming nervousness to create an impact in the minds of a target audience.

CO 3 - Aptitude: At the end of the course, the student will have acquired the ability to analyze, understand and
classify questions under arithmetic, algebra and logical reasoning and solve them employing the most suitable
methods. They will be able to analyze, compare and arrive at conclusions for data analysis questions.

CO 4 – Verbal: At the end of the course, the students will have the ability to dissect polysyllabic words, infer the
meaning, inspect, classify, contextualise and use them effectively.

CO 5 - Verbal: At the end of the course, the students will have the ability to understand the nuances of English
grammar and apply them effectively.
CO 6 – Verbal: At the end of the course, the students will have the ability to identify, analyse and interpret
relationship between words and use the process of elimination to arrive at the answer. They will also have the
ability to judge, evaluate, summarise, criticise, present and defend their perceptions convincingly.

CO-PO Mapping:

CO/PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 2 3 3 3
CO2 2 3 3
CO3 3 2

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO4 3 3
CO5 3 3
CO6 3 3 3

Soft skills and its importance: Pleasure and pains of transition from an academic environment to work -
environment. Need for change. Fears, stress and competition in the professional world. Importance of positive
attitude, Self-motivation and continuous knowledge upgradation.

Self-confidence: Characteristics of the person perceived, characteristics of the situation, characteristics of the
perceiver. Attitude, values, motivation, emotion management, steps to like yourself, positive mental attitude,
assertiveness.

Presentations: Preparations, outlining, hints for efficient practice, last minute tasks, means of effective
presentation, language, gestures, posture, facial expressions, professional attire.

Vocabulary building: A brief introduction into the methods and practices of learning vocabulary. Learning how
to face questions on antonyms, synonyms, spelling error, analogy, etc. Faulty comparison, wrong form of words
and confused words like understanding the nuances of spelling changes and wrong use of words. Listening skills:
The importance of listening in communication and how to listen actively.

Prepositions, articles and punctuation: A experiential method of learning the uses of articles and prepositions in
sentences is provided.

Problem solving level I: Number system; LCM &HCF; Divisibility test; Surds and indices; Logarithms; Ratio,
proportions and variations; Partnership;

Problem solving level II: Time speed and distance; work time problems;

Data interpretation: Numerical data tables; Line graphs; Bar charts and Pie charts; Caselet forms; Mix diagrams;
Geometrical diagrams and other forms of data representation.

Logical reasoning: Family tree; Deductions; Logical connectives; Binary logic; Linear arrangements; Circular and
complex arrangement; Conditionalities and grouping; Sequencing and scheduling; Selections; Networks; Codes;
Cubes; Venn diagram in logical reasoning; Quant based reasoning; Flaw detection; Puzzles; Cryptogrithms.

TEXTBOOKS
A Communicative Grammar of English: Geoffrey Leech and Jan Svartvik. Longman, London.
Adair. J., (1986), "Effective Team Building: How to make a winning team", London, U.K: Pan Books.
Gulati. S., (2006) "Corporate Soft Skills", New Delhi, India: Rupa& Co.
The Hard Truth about Soft Skills, by Amazone Publication.
Quantitative Aptitude by R. S. Aggarwal,S. Chand
Quantitative Aptitude – AbijithGuha, TMH.
Quantitative Aptitude for Cat - Arun Sharma. TMH.

REFERENCES:
Books on GRE by publishers like R. S. Aggrawal, Barrons, Kaplan, The Big Book, and Nova.
More Games Teams Play, by Leslie Bendaly, McGraw Hill Ryerson.
The BBC and British Council online resources
Owl Purdue University online teaching resources
www.the grammarbook.com - online teaching resources www.englishpage.com- online teaching resources and
other useful websites.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 SEMESTER V

22AIE301 PROBABILISTIC REASONING L-T-P-C: 2- 0- 3- 3

Course Objectives

 The course will lay down the basic concepts and techniques of probabilistic reasoning.

 It will explore the concepts initially through computational experiments and then try to understand the
concepts/theory behind it.

 At the same time, it will provide an appreciation of probabilistic reasoning required for AI.

Course Outcomes

After completing this course student will be able to,

CO1: Create probabilistic models pertinent to represent uncertain knowledge.

CO2: Apply the formalism of Bayesian and Markov Networks to solve real world problems.

CO3: Apply tools and techniques of probabilistic reasoning for complex decision making.

CO4: Apply modern computational tools to build probabilistic models.

CO-PO Mapping

PO/ PO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO11 PO12 PSO1 PSO2 PSO3
PSO 10
CO1 3 3 3 3 3 - - - 3 2 2 3 3 2 3
CO2 3 3 3 3 3 - - - 3 2 2 3 3 2 3
CO3 3 3 3 3 3 - - - 3 2 2 3 3 2 3
CO4 3 3 3 3 3 - - - 3 2 2 3 3 2 3

Syllabus

Uncertain Knowledge Representation, Introduction to Bayesian Networks (BNs), Representation Learning in

Bayesian Networks, Inference in Bayesian Networks, Exact and Approximate Inference, Markov Networks,
Message Passing, Learning in Markov Networks, Hidden Markov Models, Markov Random Fields (MRF),
Markov Chain-Monte Carlo Method, Decision Networks.

Text Books / Reference Books

‘Artificial Intelligence: A modern Approach’, S J Russell and P Norvig, Pearson (3rd edition), 2010.

‘Machine Learning: A Probabilistic Perspective’, Kevin Murphy and Francis Bach, Penguin Publishers, 2012

Probabilistic graphical models: principles and techniques. Koller, Daphne, and Nir Friedman. MIT press, 2009.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE302 FORMAL LANGUAGE AND AUTOMATA L-T-P-C: 2- 1- 0- 3

Course Objectives

 This course helps the students to understand discrete mathematical structures and formalism.
 This course helps the students to formalize and to formulate discrete concepts and algorithms.
 This course helps the students to understand the standard hierarchy of formal grammars and their
corresponding automata.
 This course helps the students to visualize symbolic computation with automata.
 This course helps the students to understand decidable and undecidable problems in computer science,
and appreciate the Turing thesis.

Course Outcomes
After completing this course, the students will be able to
CO1: Analyze formalisms and write formal proofs for properties
CO2: Use grammatical notations to represent sequence manipulation problems
CO3: Apply various formal grammars to the problem-solving avenues
CO4: Identify limitations of some computational models and possible methods of proving them

CO-PO Mapping
PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 2 3 3 3 3 2 - - 3 2 3 2 3 1 1
CO2 3 3 3 3 3 3 - - 2 3 3 3 3 1 1
CO3 3 3 3 3 3 3 - - 2 2 3 3 3 2 2
CO4 3 3 3 3 3 3 - - 2 2 3 3 3 2 1

Syllabus

Unit 1
Introduction to Automata and formal language - Finite State machines – Deterministic finite state machine – Non-
Deterministic finite state machine- Equivalence of NFA and DFA –Minimization of Finite State Machine –
Regular Expression -Regular Language – Properties of Regular Languages.

Unit 2
Context Free Grammar -Pushdown Automata – Variants of Pushdown automata – Derivations Using a Grammar,
Leftmost and Rightmost Derivations, the Language of a Grammar, Sentential Forms, Parse Tress Equivalence
between PDA and CFG- Context Free Languages – Properties of CFL – Normal Forms.

Unit 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Context Sensitive Language- Linear Bound Automata- Turing Machine – Variants of Turing Machine –
Decidability- Post correspondence problem – Introduction to undecidable problems.

Textbooks/References
Peter Linz, Introduction to Formal Languages and Automata, 6Th Edn by, Jones & Bartlett, 2016.
J.E.Hopcroft, R.Motwaniand andJ.D.Ullman, , Introduction to Automata Theory, Languages and Computation’,
Pearson, 2001
H.R.Lewis and C.H.Papadimitriou , Elements of the Theory of Computation’, , Prentice Hall, 1997/Pearson
1998
Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE303 DATABASE MANAGEMENT SYSTEMS L-T-P-C: 2- 1- 3- 4

Course Objectives

 This course aims to understand the concepts of database design, database languages, database-system
implementation and maintenance.
 The course will provide knowledge of the design and development of databases for AI applications
using SQL and python
 The course will provide an understanding of various databases system including modern databases
systems apt for AI and ML applications

Course Outcomes
After completing this course, the students will be able to
CO1: Formulate relational algebraic expressions, SQL and PL/SQL statements to query relational databases.
CO2: Build ER models for real world databases.
CO3: Design a normalized database management system for real world databases.
CO4: Apply the principles of transaction processing and concurrency control.
CO5: Use high-level right database for AI and ML applications.

CO-PO Mapping
PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 3 2 3 3 - - - - - - - - - 1
CO2 1 3 3 3 3 - - - - - - - - - 1
CO3 2 3 2 3 - - - 2 2 2 2 - - 1 2
CO4 1 1 1 2 - - - - - - - - - - -
CO5 1 1 - - - - - - - - - - - 1 2

Syllabus

Unit 1
Introduction: Overview of DBMS fundamentals – Overview of Relational Databases and Keys. Relational Data
Model: Structure of relational databases – Database schema – Formal Relational Query Languages – Overview
of Relational Algebra and Relational Operations. Database Design: Overview of the design process - The E-R

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Models – Constraints - Removing Redundant Attributes in Entity Sets - E-R Diagrams - Reduction to Relational
Schemas - Entity Relationship Design Issues - Extended E-R Features – Alternative E-R Notations – Overview
of Unified Modelling Language (UML).

Unit 2
Relational Database Design: Features of Good Relational Designs - Atomic Domains and 1NF - Decomposition
using Functional Dependencies: 2NF, 3NF, BCNF and Higher Normal Forms. Functional Dependency Theory -
Algorithm for Decomposition – Decomposition using multi-valued dependency: 4NF and 4NF decomposition.
Database design process and its issues. SQL: review of SQL – Intermediate SQL – Advanced SQL.

Unit 3
Transactions: Transaction concept – A simple transaction model - Storage structure - Transaction atomicity and
durability - Transaction isolation – Serializability – Recoverable schedules, Casecadeless schedules. Concurrency
control: Lock-based protocols – Locks, granting of locks, the two-phase locking protocol, implementation of
locking, Graph-based protocols. Deadlock handling: Deadlock prevention, Deadlock detection and recovery.
Case Study: Different types of high-level databases – MongoDB, Hadoop/Hbase, Redis, IBM Cloudant, Dynamo
DB, Cassandra and Couch DB etc. Tips for choosing the right database for the given problem.

Textbooks/References
Silberschatz A, Korth HF, Sudharshan S. Database System Concepts. Sixth Edition, TMH publishing company
limited; 2011.
Garcia-Molina H, Ullman JD, Widom J. Database System; The complete book. Second Edition, Pearson
Education India, 2011
Elmasri R, Navathe SB. Fundamentals of Database Systems. Fifth Edition, Addison Wesley

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 3) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE304 DEEP LEARNING L-T-P-C: 2- 0- 3- 3

Course Objectives

 This course provides the basic concepts of deep learning and implementation using Matlab/Python.
 This course provides the application of deep learning algorithms in signal and image data analysis.
 This course covers the concept of deep learning algorithms such as transfer learning and attention models
for signal and image analysis.

Course Outcomes (CO)

After completing this course students will be able to,

CO1: Apply the fundamentals of deep learning.

CO2: Apply deep learning algorithms using Matlab/Python.

CO3: Apply deep learning models for signal analysis.

CO4: Implement deep learning models for image analysis.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO-PO Mapping

PO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO

CO1 3 2 2 - 3 2 - - 3 3 - 3 2 3 2

CO2 3 2 2 2 3 3 - - 3 3 2 3 3 3 2

CO3 3 2 2 2 3 3 - - 3 3 2 3 3 3 2

CO4 3 3 2 2 3 3 - - 3 3 2 3 3 3 3

Syllabus

Unit 1

Deep Neural Networks (DNN) –Convolutional Neural Network (CNN) – Recurrent Neural Network (RNN):
Long-Short- Term-Memory (LSTM) - Graph based Neural Network (GNN)

Unit 2

Pre-processing: Noise Removal using deep learning algorithms - Feature Extraction - Signal Analysis: Time Series
Analysis, CNNs, Auto encoders.

Unit 3

Image Analysis: Transfer Learning, Attention models- Ensemble Methods for Signal and Image Analysis.

Text Books / Reference Books

Bishop C.M, “Pattern Recognition and Machine Learning”, Springer, 1st Edition, 2006.

Goodfellow I, Bengio Y, Courville A, & Bengio Y, “Deep learning”, Cambridge: MIT Press, 1st Edition, 2016.
Soman K.P, Ramanathan. R, “Digital Signal and Image Processing – The Sparse Way”, Elsevier, 1st Edition,
2012.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 22AIE305 INTRODUCTION TO CLOUD COMPUTING L-T-P-C: 2- 0- 3- 3

Course Objectives
 This course introduces the basic principles of cloud computing, cloud native application development
and deployment, containerization principles, micro-services and application scaling.
 This course will also equip the students to understand major industry players in the public cloud domain
for application development and deployment.

Course Outcomes
After completing this course, the students will be able to
CO1: Demonstrate the functionalities of cloud computing.
CO2: Apply cloud native application development for containerization and container orchestration.
CO3: Analyze different types of cloud services – Delivery models, Deployment models.
CO4: Implement different solution approaches in Cloud – containers in public cloud, setting up private cloud
and convert monolithic applications to containers.

CO-PO Mapping
PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 1 1 - - - - - - - 1 - - - -
CO2 3 2 2 2 3 2 3 2 2 2 2 2 - 1 2
CO3 3 2 2 2 3 2 3 2 1 - 2 - - 2 3
CO4 3 2 2 2 3 2 3 2 2 2 2 2 - 2 3

Syllabus

Unit 1
Distributed Computing Taxonomy – Cluster, Grid, P2P, Utility, Cloud, Edge, Fog computing paradigms;
Introduction to Cloud Computing – Cloud delivery models (XaaS), Cloud deployment models (Private, Public,
Hybrid); Characteristics of Cloud, Major use cases of Cloud; disadvantages and best practices; Major public cloud
players in the market; Security Issues and Challenges; Cloud Native application development – Introduction to
JavaScript Cloud native application development

Unit 2
Public Cloud – Using public cloud for infrastructure management (compute and storage services), Web
application deployment using public cloud services, and Deploying container images in public cloud, Overview
of cognitive services, Case study on architecting cloud-based solutions for a chosen scenario.

Unit 3
Virtualization – Basics, Cloud vs Virtualization, Types of virtualizations, Hypervisor types; Containers –
Introduction to dockers and containers, containerization vs virtualization, docker architecture, Use cases, Learn
how to build container images, Operations on container images; Kubernetes – Need for orchestration, container
orchestration methods, Introduction to Kubernetes, Kubernetes architecture, using YAML file, Running
Kubernetes via minikube.

Textbooks/References
Rajkumar Buyya et.al. Mastering cloud computing, McGraw Hill Education;2013.
Matthias K, Kane SP. Docker: Up & Running: Shipping Reliable Containers in Production. " O'Reilly Media,
Inc."; 2018.
Gift, Noah. Pragmatic AI: An Introduction to Cloud-based Machine Learning. Addison-Wesley Professional,
2018
Kocher PS. Microservices and Containers. Addison-Wesley Professional; 2018.
Sarkar A, Shah A. Learning AWS: Design, build, and deploy responsive applications using AWS Cloud
components. Packt Publishing Ltd; 2018.
Menga J. Docker on Amazon Web Services: Build, deploy, and manage your container applications at scale.
Packt Publishing Ltd; 2018.
Bentley W. OpenStack Administration with Ansible 2. Packt Publishing Ltd; 2016

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

19LIV390 LIVE-IN-LAB I L-T-P-C: 0-0-0-3

Course Objectives
 Identify and analyse the various challenge indicators present in the village by applying concepts of
Human Centered Design and Participatory Rural Appraisal.
 User Need Assessment through Quantitative and Qualitative Measurements
 Designing a solution by integrating Human Centered Design concepts
 Devising proposed intervention strategies for Sustainable Social Change Management

Course Outcome

CO1: Learn ethnographic research and utilise the methodologies to enhance participatory engagement.
CO2: Prioritize challenges and derive constraints using Participatory Rural Appraisal.
CO3: Identify and formulate the research challenges in rural communities.
CO4: Design solutions using human centered approach.

CO-PO Mapping

PO/PSO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12
CO
CO1 3 3 1 1 3 3 3
CO2 3 3 3 3
CO3 3 1 3 3 3
CO4 3 3 3 3 3 3 3

Syllabus

This initiative is to provide opportunities for students to get involved in coming up with technology solutions for
societal problems. The students shall visit villages or rural sites during the vacations (after 4th semester) and if
they identify a worthwhile project, they shall register for a 3-credit Live-in-Lab project, in the fifth semester.
Thematic Areas
• Agriculture & Risk Management
• Education & Gender Equality

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

• Energy & Environment
• Livelihood & Skill Development
• Water & Sanitation
• Health & Hygiene
• Waste Management & Infrastructure

The objectives and the projected outcome of the project will be reviewed and approved by the department
chairperson and a faculty assigned as the project guide.

Evaluation Pattern

Assessment Marks
Internal (Continuous Evaluation) [75 marks]
Workshop (Group Participation) 15
Village Visit Assignments & Reports 15
Problem Identification and Assessment 15
Ideation: Defining the Needs, Proposed
20
Designs & Review
Poster Presentation 10
External [25 marks]
Research Paper Submission 25
Total 100
Attendance (To be added separately) 5
Grand Total 105

19SSK301 SOFT SKILLS II L-T-P-C: 1-0-3-2

Course Outcomes

CO # 1 - Soft Skills: At the end of the course, the students will have the ability to communicate convincingly and
negotiate diplomatically while working in a team to arrive at a win-win situation. They would further develop their inter-
personal and leadership skills.

CO # 2 - Soft Skills: At the end of the course, the students shall learn to examine the context of a Group Discussion topic
and develop new perspectives and ideas through brainstorming and arrive at a consensus.

CO # 3 - Aptitude: At the end of the course, students will be able to identify, recall and arrive at appropriate strategies
to solve questions on geometry. They will be able to investigate, interpret and select suitable methods to solve questions
on arithmetic, probability and combinatorics.

CO # 4 – Verbal: At the end of the course, the students will have the ability to relate, choose, conclude and determine
the usage of right vocabulary.

CO # 5 - Verbal: At the end of the course, the students will have the ability to utilise prior knowledge of grammar to
recognise structural instabilities and modify them.
CO # 6 – Verbal: At the end of the course, the students will have the ability to comprehend, interpret, deduce and
logically categorise words, phrases and sentences. They will also have the ability to theorise, discuss, elaborate, criticise
and defend their ideas.

Syllabus

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Professional grooming and practices: Basics of corporate culture, key pillars of business etiquette. Basics of
etiquette: Etiquette – socially acceptable ways of behaviour, personal hygiene, professional attire, cultural
adaptability. Introductions and greetings: Rules of the handshake, earning respect, business manners. Telephone
etiquette: activities during the conversation, conclude the call, to take a message. Body Language: Components,
undesirable body language, desirable body language. Adapting to corporate life: Dealing with people.
Group discussions: Advantages of group discussions, structured GD – roles, negative roles to be avoided,
personality traits to do well in a GD, initiation techniques, how to perform in a group discussion, summarization
techniques.
Listening comprehension advanced: Exercise on improving listening skills, grammar basics: Topics like clauses,
punctuation, capitalization, number agreement, pronouns, tenses etc.
Reading comprehension advanced: A course on how to approach middle level reading comprehension passages.
Problem solving level III: Money related problems; Mixtures; Symbol based problems; Clocks and calendars;
Simple, linear, quadratic and polynomial equations; special equations; Inequalities; Functions and graphs;
Sequence and series; Set theory; Permutations and combinations; Probability; Statistics.
Data sufficiency: Concepts and problem solving.
Non-verbal reasoning and simple engineering aptitude: Mirror image; Water image; Paper folding; Paper cutting;
Grouping of figures; Figure formation and analysis; Completion of incomplete pattern; Figure matrix;
Miscellaneous.
Spatial aptitude: Cloth, leather, 2D and 3D objects, coin, match sticks, stubs, chalk, chess board, land and geodesic
problems etc., related problems.

TEXTBOOK(S)
A Communicative Grammar of English: Geoffrey Leech and Jan Svartvik. Longman, London.
Adair. J., (1986), "Effective Team Building: How to make a winning team", London, U.K: Pan Books.
Gulati. S., (2006) "Corporate Soft Skills", New Delhi, India: Rupa& Co.
The Hard Truth about Soft Skills, by Amazone Publication.
Quick Maths – Tyra.
Quicker Arithmetic – Ashish Aggarwal
Test of reasoning for competitive examinations by Thorpe.E. TMH
Non-verbal reasoning by R. S. Aggarwal, S. Chand

REFERENCE(S)
Books on GRE by publishers like R. S. Aggrawal, Barrons, Kaplan, The Big Book, and Nova
More Games Teams Play, by Leslie Bendaly, McGraw Hill Ryerson.
The BBC and British Council online resources
Owl Purdue University online teaching resources
www.the grammarbook.com - online teaching resources www.englishpage.com- online teaching resources and
other useful websites.

SEMESTER VI

22AIE311 SOFTWARE ENGINEERING (PROJECT BASED) L-T-P-C: 2- 0- 3- 3

Course Objectives
 This course presents a broad perspective on software systems engineering, concentrating on
widely used techniques for developing large-scale software systems.

 This course covers a wide spectrum of software processes from initial requirements elicitation through design
and development to system evolution.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 This course also covers a wide range of software development abilities and skills from analysing a problem to
implementing a solution.

Course Outcomes

After completing this course, the students will be able to

CO1: Understand the basic principles of Software Engineering.

CO2: Understand how to choose the appropriate SDLC models depending on the user requirements.

CO3: Understand the concept of Requirements Engineering.

CO4: Understand the concept of Software Design.

CO5: Understand how to apply the knowledge, techniques, and skills in the development of software and its
maintenance.

CO-PO Mapping

PSO/PO PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO

1 2 3 4 5 6 7 8 9 0 1 2 1 2 3

CO
CO1 3 2 1 1 1
CO2 3 3 2 2 2 3 3
CO3 3 3 2 1 2 3 3
CO4 3 2 3 2 2 2 1 2 3
CO5 3 2 2 2 2 2 2 2 1 1 3

Syllabus
Unit-1

Introduction to Software Engineering: Introduction, Software Failures, Software Crisis, Classification of

Software, Software characteristics, Software Engineering-A Layered Technology, Basic of Life Cycle,
Software Life Cycle Models: Waterfall Model, V-Model, Prototype Model, Incremental Model, Iterative
Model, Evolutionary Process Model, Spiral Model, Agile Manifesto, Principles of the agile manifesto, Various
Agile methodologies: Scrum, Extreme programming.

Unit-2
Requirement Engineering: Basic concepts of Requirements Analysis and Specification, Role of a system
analyst, SRS document and its important parts, properties of a good SRS document, functional requirements,
non-functional requirements, decision tree, and decision table.

Design Engineering: Basic Concepts of Software Design, Preliminary and detailed design, Characteristics of
a good software design, cohesion and its types, coupling and its types, function-oriented design approach, and
object-oriented design approach. Data Flow Diagrams, Structured Design.

Unified Modelling Language (UML): Basic concepts of UML, Different types of diagrams, and views
supported in UML.

Unit-3

User interface design: Basic concepts of user interface design, Types of User Interfaces.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Coding and Testing: Coding- Coding Standards and Guidelines, Code Review- Code Walkthrough, Code
Inspection, Clean Room Testing. Testing- Basic Concepts of Testing and Terminologies, Testing Activities,
Why Design Test Cases?, Testing in the Large versus Testing in the Small, Unit Testing, Black-box Testing-
Black-box Test Suite Design Approach: Equivalence Class Partitioning, Boundary Value Analysis, White-
Box Testing-Basic concept, statement coverage, branch coverage, Multiple Condition Coverage, Path
Coverage, McCabe’s Cyclomatic Complexity Metric, Data Flow-based Testing, Mutation Testing, Grey-Box
Testing, Integration Testing, System Testing, Smoke Testing- Performance Testing, Error Seeding, Some
General Issues Associated with Testing. Debugging techniques. Software Documentation- Internal
Documentation, External Documentation.

Text Books
Pressman R S, Bruce R.Maxim, Software Engineering - A Practitioner’s Approach. Eighth Edition, McGraw-
Hill Education, 2019.

Rajib Mall, “Fundamentals of Software Engineering”, Fifth Edition, PHI

Reference Books

Pankaj Jalote's, Software Engineering: A Precise Approach, Wileyindia, 2010

Crowder JA, Friess S. Agile project management: managing for success. Cham: Springer International
Publishing; 2015.

Stellman A, Greene J. Learning agile: Understanding scrum, XP, lean, and kanban. " O'Reilly Media, Inc.";
2015.
Gregory J, Crispin L. More agile testing: learning journeys for the whole team. Addison-Wesley Professional;
2015.

Rubin KS. Essential Scrum: a practical guide to the most popular agile process. Addison-Wesley; 2012.

Cohn M. User stories applied: For agile software development. Addison-Wesley Professional; 2004

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 22AIE312 BIG DATA ANALYTICS L-T-P-C: 2- 0- 3- 3

Course Objectives

 To understand how to use Big data frameworks and APIs.

 To conceptualize data analysis.
 To learn about various data processing and pipelining strategies.
 To understand and visualize map-reduce computing paradigm.
 To learn the intricate and distributed working of Big Data clusters
 To train and impart the skills required for managing and balancing large data clusters

Course Outcomes (CO)

After completing this course student will be able to,

CO1: Solve problems through map-reduce approach

CO2: Implement data analytics solutions using general data pipelining

CO3: Apply scaling up machine learning techniques and associated computing techniques and technologies.
CO4: Identify the characteristics of datasets and compare the trivial data and big data for various
applications.

CO-PO Mapping

PO/PS
O PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO
1 2 3 4 5 6 7 8 9 0 1 2 1 2 3
CO

CO1 3 3 3 3 3 1 - - 2 2 3 2
3 2 1
CO2 3 3 3 3 3 2 - - 3 3 3 3 2 3 2
CO3 3 3 3 3 3 1 - - 2 3 3 2
3 3 3
CO4 2 2 3 2 3 1 - - 2 2 2 2 1 1 3

Syllabus

UNIT 1

Classification of Digital Data, Structured and Unstructured Data – Introduction to Big Data: Characteristics –
Evolution – Definition, - Data Warehouse, Hadoop ecosystem in Brief, Map Reduce: Mapper – Reducer –
Combiner – Partitioner – Searching – Sorting – Compression -Terminologies used in Big Data Environments -
NoSQL, Comparison of SQL and NoSQL, Distributed Computing Challenges - Hadoop Distributed File System
- Processing Data with Hadoop - Basically Available Soft State Eventual Consistency, programming paradigm -
Functional Programming in Scala: Basic Syntax-type inference- Parameters-Recursive arbitrary collections –
ConsList-Arrays-Tail recursion- Higher order functions

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

UNIT 2

MapReduce Template-Pattern Matching syntax, objects in Scala. Apache Spark: -Resilient Distributed Datasets -
Creating RDDs, Lineage and Fault toleranc, DAGs, Immutability, task division and partitions, transformations
and actions, lazy evolutions and optimization -Formatting and housing data from spark RDDs--Persistence.

UNIT 3

Data frames, datasets, Apache Spark SQL, Setting up a standalone Spark cluster-: spark-shell, basic API,
Modules-Core, Key/Value pairs and other RDD features, MLlib-examples for bi-class SVM and logistic
regression.

UNIT 4

MongoDB: Why Mongo DB - Terms used in RDBMS and Mongo DB - Data Types - MongoDB Query Language.
Stream and Graph Processing on Spark.

Text Books / Reference Books

Learning Spark: Lightning-Fast Big Data Analysis’, Holden Karau , Andy Konwinski, Patrick Wendell and
MateiZaharia, O′Reilly; 1st edition , 2015

‘Programming in Scala: A Comprehensive Step-by-Step Guide’, Martin Odersky,Lex Spoon andBill Venners,
Artima Inc; Version ed. edition , 2008

‘High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark’, Holden Karau, Rachel
Warren, O′Reilly; 1st edition, 2017
‘Scala for the Impatient’, Cay S. Horstmann, Addison-Wesley; 2nd edition, 2017

“Mongo DB in Action”, Kyle Banker, Manning Publications; 2nd edition, 2016

“MongoDB: The Definitive Guide”, Shannon Bradshaw, Eoin Brazil, Kristina Chodorow, O′Reilly; 3rd edition,
2019

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE313 COMPUTER VISION AND IMAGE PROCESSING L-T-P-C: 2- 1- 3- 4

Course Objectives

 This course introduces the geometry of image formation and its use for 3D reconstruction and calibration.
 This course introduces the analysis of patterns in visual images that are used to reconstruct and
understand objects and scenes.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Course Outcomes
After completing this course, the students will be able to
CO1: Apply image formation and camera calibration for various applications.
CO2: Analyze and select image features and apply for image matching.
CO3: Develop image recognition algorithms.
CO4: Develop stereo vision applications for distance estimation.

CO-PO Mapping
PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 3 2 - 2 2 1 1 1 - - - 1 1 -
CO2 3 3 2 3 3 3 2 1 2 1 - - - 1 2
CO3 3 3 3 3 3 3 2 3 3 3 - - - 2 3
CO4 3 3 1 2 3 2 1 1 1 1 - - - - -

Syllabus

Unit 1
Introduction, Image Formation – geometric primitives and transformations, photometric image formation, digital
camera, Camera calibration. Edge Detection, Segmentation.

Unit 2
Feature Detection and Matching – points and patches, edges, lines, Feature-Based Alignment - 2D, 3D feature-
based alignment, pose estimation, Image Stitching, Dense motion estimation – Optical flow - layered motion,
parametric motion, Structure from Motion.

Unit 3
Recognition – object detection, face recognition, instance recognition, category recognition, Stereo
Correspondence – Epipolar geometry, 3D reconstruction.

Textbooks/References
Szeliski R. Computer Vision: Algorithms and Applications Springer. New York. 2010..
Shapiro LG, Stockman GC. Computer Vision: Theory and Applications. 2001.
Forsyth DA, Ponce J. Computer Vision: a modern approach;2012.
Davies ER. Machine vision: theory, algorithms, practicalities. Elsevier; 2004 Dec 22.
Jain R, Kasturi R, Schunck BG. Machine vision. New York: McGraw-Hill; 1995 Mar 1

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 3) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 22AIE314 COMPUTER SECURITY L-T-P-C: 2- 0- 3- 3

Course Objective
 This course provides basic knowledge and skills in the fundamental theories and practices of cyber
security.
 This course provides an overview of the field of security and assurance emphasizing the need to protect
information being transmitted electronically.

Course Outcomes
After completing this course, the students will be able to
CO1: Implement cryptographic techniques in secure application development
CO2: Apply methods for authentication, access control, intrusion detection and prevention
CO3: Apply fundamental security principles to analyze threat situations
CO4: Design mechanisms to provide security in a network

CO-PO Mapping
PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 3 2 - 2 2 1 1 1 - - - 1 1 -
CO2 3 3 2 3 3 3 2 1 2 1 - - - 1 2
CO3 3 3 3 3 3 3 2 3 3 3 - - - 2 3
CO4 3 3 1 2 3 2 1 1 1 1 - - - - -

Syllabus

Unit 1
Basics of Computer Security: Overview – Definition of terms – Security goals – Shortcomings – Attack and
defence – Malicious code – Worms – Intruders – Error detection and correction Encryption and Cryptography:
Ciphers and codes – Public key algorithms – Key distribution – Digital signatures.

Unit 2
Security Services: Authentication and Key Exchange Protocols - Access control matrix – User authentication –
Directory authentication service – Diffie-Hellman key exchange – Kerberos.

Unit 3
System security and Security models: Disaster recovery - Protection policies. E-mail Security: Pretty good privacy
- Database Security: Integrity constraints - multi-phase commit protocols - Networks Security: Threats in networks
- DS authentication -Web and Electronic Commerce: Secure socket layer - Client-side certificates - Trusted
Systems: Memory protection.

Textbooks/References
William Stallings, Lawrie Brown, "Computer Security: Principles and Practice", Prentice Hall, 4th edition
Stallings William, Cryptography and Network Security: Principles and Practice, 7th Edition, Pearson/Prentice-
Hall, 2018.
Forouzan B A, Cryptography and Network Security, Special Indian Edition, Tata McGraw Hill, 2007.
Padmanabhan TR, Shyamala C K, and Harini N, Cryptography and Security, First Edition, Wiley India
Publications, 2011

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE315 NATURAL LANGUAGE PROCESSING L-T-P-C: 2- 0- 3- 3

Course Objectives
 The main objective of the course is to understand the leading trends and systems in Natural Language
Processing.
 This course will help the students to understand the basic representations used in syntax, the semantics
of Natural Language Processing.
 This course will help the students to understand and explore the models used for word/sentence
representations for various NLP applications.
 This course will help the students to implement deep learning algorithms in Python and learn how to
train deep networks for NLP applications.

Course Outcomes

After completing this course, students will be able to

CO1: Apply modern tools for solving problems in computational linguistics
CO2: Implement word representation models to solve NLP problems
CO3: Develop deep learning models for solving NLP applications
CO4: Evaluate the performance of NLP models

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 3 2 3 3 1 - 1 3 3 - 3 3 3 3

CO2 3 3 2 3 3 1 - 1 3 3 - 3 3 3 3

CO3 3 3 2 3 3 1 - 1 3 3 - 3 3 3 3

CO4 - - 1 2 1 1 - 1 3 3 - 2 - 1 1

Syllabus

Unit 1
Computational linguistics- Introduction, syntax, semantics, morphology, collocation and other NLP problems.

Unit 2
Word representation: One-hot encoding, Bag-of-Words (BoW) Dictionary: Term Frequency – Inverse
Document Frequency (TF-IDF), Language Model-n-gram – Neural Network-based word embedding algorithms

Unit 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Sequences and sequential data: Machine learning and deep learning for NLP, Recurrent Neural Network, Long
Short-Term Memory networks, Gated Recurrent Unit - Sequence to sequence modelling - Encoder decoder -
Attention mechanism, Transformer Networks – BERT, GPT, Graph NLP, Hidden Markov Model, Conditional
Random Field, Topic modelling

Unit 4
Applications of NLP: Part-of-Speech tagging, Named Entity recognition, Dependency parsing, - Sentiment
Analysis, Machine translation, Question answering, Text summarization, Evaluation metrics for NLP models
and Visualization

Text Books / References

Daniel Jurafsky, James H Martin, Speech & language processing, preparation [cited 2020 June 1] Available
from: https://web. stanford. edu/~ jurafsky/slp3 (2018).
Christopher Manning and Hinrich Schütze, F oundations of Statistical Natural Language Processing, MIT
press, 1999.
Steven Bird, Ewan Klein and Edward Loper, Natural Language Processing with Python, O'Reilly Media, Inc.,
2009.
Jason Browlee, Deep Learning for Natural Language Processing: Develop Deep Learning Models for your
Natural Language Problems (Ebook), Machine Learning Mastery, 2017

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

19LIV490 LIVE-IN-LAB II L-T-P-C: 0-0-0-3

Course Objectives

 Proposal writing in order to bring in a detailed project planning, enlist the materials required and propose
budget requirement.
 Use the concept of CoDesign to ensure User Participation in the Design Process in order to rightly capture
user needs/requirements.
 Building and testing a prototype to ensure that the final design implementation is satisfies the user needs,
feasible, affordable, sustainable and efficient.
 Real time project implementation in the village followed by awareness generation and skill training of
the users (villagers)

Course Outcome

CO1: Learn co-design methodologies and engage participatorily to finalise a solution

CO2: Understand sustainable social change models and identify change agents in a community.
CO3: Learn Project Management to effectively manage the resources
CO4: Lab scale implementation and validation
CO5. Prototype implementation of the solution

CO-PO Mapping

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 PO/PSO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12
CO
CO1 1 1 3 3 1 3 3 3 3
CO2 3 3
CO3 3 3 3
CO4 3 3 3 1 3 3 3 3
CO5 1 3 3

Syllabus

The students shall visit villages or rural sites during the vacations (after 6th semester) and if they identify a
worthwhile project, they shall register for a 3-credit Live-in-Lab project, in the fifth semester.
Thematic Areas
• Agriculture & Risk Management
• Education & Gender Equality
• Energy & Environment
• Livelihood & Skill Development
• Water & Sanitation
• Health & Hygiene
• Waste Management & Infrastructure

Evaluation Pattern

Assessment Marks
Internal (Continuous Evaluation) [63 marks]
1. Proposed Implementation
2
Presentation Round 1
2. Proposal Submission + Review 6
3. Co-design 6
i. Village Visit I (Co-Design Field
4
Work Assignments)
ii. Presentation of Co-design
2
Assessment
4. Prototype Design 14
i. Prototype Design 4
ii. Prototype Submission 8
iii. Sustenance Plan 2
5. Implementation 35
i. Implementation Plan Review 3
ii. Implementation 24
iii. Testing & Evaluation 4
iv. Sustenance Model Implementation 4
External [37 marks]
6. Research Paper 18
7. Final Report 15
8. Poster Presentation 4
Total 100

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Attendance 5
Grand Total 105

Course Outcomes:

CO # 1 - Soft Skills: At the end of the course, the students will have the ability to prepare a suitable resume
(including video resume). They would also have acquired the necessary skills, abilities and knowledge to present
themselves confidently. They would be sure-footed in introducing themselves and facing interviews.
CO # 2 - Soft Skills: At the end of the course, the students will have the ability to analyse every question asked
by the interviewer, compose correct responses and respond in the right manner to justify and convince the
interviewer of one’s right candidature through displaying etiquette, positive attitude and courteous
communication.
CO # 3 - Aptitude: At the end of the course, students will be able to interpret, critically analyze and solve logical
reasoning questions. They will have acquired the skills to manage time while applying methods to solve questions
on arithmetic, algebra, logical reasoning, and statistics and data analysis and arrive at appropriate conclusions.

CO # 4 – Verbal: At the end of the course, the students will have the ability to understand and use words, idioms
and phrases, interpret the meaning of standard expressions and compose sentences using the same.
CO # 5 - Verbal: At the end of the course, the students will have the ability to decide, conclude, identify and
choose the right grammatical construction.

CO # 6 – Verbal: At the end of the course, the students will have the ability to examine, interpret and investigate
arguments, use inductive and deductive reasoning to support, defend, prove or disprove them. They will also have
the ability to create, generate and relate facts / ideas / opinions and share / express the same convincingly to the
audience / recipient using their communication skills in English.

Team work: Value of team work in organisations, definition of a team, why team, elements of leadership,
disadvantages of a team, stages of team formation. Group development activities: Orientation, internal problem
solving, growth and productivity, evaluation and control. Effective team building: Basics of team building,
teamwork parameters, roles, empowerment, communication, effective team working, team effectiveness criteria,
common characteristics of effective teams, factors affecting team effectiveness, personal characteristics of
members, team structure, team process, team outcomes.

Facing an interview: Foundation in core subject, industry orientation / knowledge about the company,
professional personality, communication skills, activities before interview, upon entering interview room, during
the interview and at the end. Mock interviews.

Advanced grammar: Topics like parallel construction, dangling modifiers, active and passive voices, etc.

Syllogisms, critical reasoning: A course on verbal reasoning. Listening comprehension advanced: An exercise
on improving listening skills.

Reading comprehension advanced: A course on how to approach advanced level of reading, comprehension
passages. Exercises on competitive exam questions.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Problem solving level IV: Geometry; Trigonometry; Heights and distances; Co-ordinate geometry; Mensuration.

Specific training: Solving campus recruitment papers, national level and state level competitive examination
papers; Speed mathematics; Tackling aptitude problems asked in interview; Techniques to remember (In
mathematics). Lateral thinking problems. Quick checking of answers techniques; Techniques on elimination of
options, estimating and predicting correct answer; Time management in aptitude tests; Test taking strategies.

TEXTBOOK(S)

A Communicative Grammar of English: Geoffrey Leech and Jan Svartvik. Longman, London.
Adair. J., (1986), "Effective Team Building: How to make a winning team", London, U.K: Pan Books.
Gulati. S., (2006) "Corporate Soft Skills", New Delhi, India: Rupa& Co.
The Hard Truth about Soft Skills, by Amazone Publication.
Data Interpretation by R. S. Aggarwal, S. Chand
Logical Reasoning and Data Interpretation – Niskit K Sinkha
Puzzles – Shakuntala Devi
Puzzles – George J. Summers.

REFERENCE(S)

Books on GRE by publishers like R. S. Aggrawal, Barrons, Kaplan, The Big Book, and Nova.
More Games Teams Play, by Leslie Bendaly, McGraw-Hill Ryerson.
The BBC and British Council online resources
Owl Purdue University online teaching resources

www.the grammarbook.com - online teaching resources www.englishpage.com- online teaching resources and
other useful websites.

SEMESTER VII

22AIE401 REINFORCEMENT LEARNING L-T-P-C: 2- 0- 3- 3

Course Objectives
 This course will provide a solid introduction to the field of reinforcement learning.
 It will also make the students learn about the core challenges and approaches, including exploration and
exploitation.
 The course will make the students well versed in the key ideas and techniques for reinforcement learning

Course Outcomes

After completing this course, the students will be able to

CO1: Define the key features of reinforcement learning that distinguishes it from AI and non-interactive
machine learning

CO2: Decide if an application problem should be formulated as a RL problem; if yes be able to define it
formally (in terms of the state space, action space, dynamics and reward model), state what algorithm
(from class) is best suited for addressing it

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO3: Implement in code common RL algorithms

CO4: Describe (list and define) multiple criteria for analysing RL algorithms and evaluate algorithms on these
metrics: e.g., regret, sample complexity, computational complexity, empirical performance,
convergence, etc.

CO5: Describe the exploration vs exploitation challenge and compare and contrast at least two approaches for
addressing this challenge (in terms of performance, scalability, complexity of implementation, and
theoretical guarantees)

CO-PO Mapping

PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO

1 2 3 4 5 6 7 8 9 0 1 2 1 2 3

CO 3 3 3 3 3 - - - 3 2 3 3 3 3 3
1

CO 3 3 3 3 3 - - - 3 2 3 3 3 3 3
2

CO 3 3 3 3 3 - - - 3 2 3 3 3 3 3
3

CO 3 3 3 3 3 - - - 3 2 3 3 3 3 3
4

CO 3 3 3 3 3 - - - 3 2 3 3 3 3 3
5

Syllabus
Introduction to Reinforcement Learning – Elements of Reinforcement Learning – Multi-armed Bandits – Finite
Markov Decision Processes – Dynamic Programming – Monte Carlo Methods – Temporal-Difference Learning
– n-step Bootstrapping - Planning and Learning with Tabular Methods.

Text Books / Reference Books

‘Reinforcement Learning’, Richard.S.Sutton and Andrew G.Barto, Second edition, MIT Press, 2018

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

19LAW300 INDIAN CONSTITUTION L-T-P-C: P/F

Course Objective

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  To know about Indian constitution.
 To know about central and state government functionalities in India
 To know about Indian society

Course Outcomes

CO1: Understand the functions of the Indian government

CO2: Understand and abide the rules of the Indian constitution
CO3: Understand and appreciate different culture among the people

CO-PO Mapping

PO/PSO PSO3
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
CO
CO1 - - - - - 3 2 3 - - - - - - -
CO2 - - - - - 3 2 3 - - - - - - -
CO3 - - - - - 3 2 3 - - - - - - -

Syllabus

Unit 1
Historical Background – Constituent Assembly Of India – Philosophical Foundations Of The Indian Constitution
– Preamble – Fundamental Rights – Directive Principles Of State Policy – Fundamental Duties – Citizenship –
Constitutional Remedies For Citizens.

Unit 2
Union Government – Structures of the Union Government and Functions – President – Vice President – Prime
Minister – Cabinet – Parliament – Supreme Court of India – Judicial Review.

Unit 3
State Government – Structure and Functions – Governor – Chief Minister – Cabinet – State Legislature – Judicial
System in States – High Courts and other Subordinate Courts.

Text Book(s)

Durga Das Basu, “Introduction to the Constitution of India “, Prentice Hall of India, New Delhi.
R.C.Agarwal, (1997) “Indian Political System”, S.Chand and Company, New Delhi.

Reference(s)

Sharma, Brij Kishore, “Introduction to the Constitution of India”, Prentice Hall of India, New Delhi.

Evaluation Pattern

Assessment Internal External

Online Test - 100
P/F

22AIE498 PROJECT PHASE - 1 L-T-P-C: 0- 0- 18- 6

Course Objectives

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  Project Phase – 1 aims at helping students to identify the research problems by conducting a thorough
literature review
 The course introduces the students to real world problems associated with AI
 The course also aims at helping students to publish scientific articles in peer reviewed scientific
publications.

Course Outcomes

After completing the course, the students will be able to

CO1: Identify a valid research problem by conducting literature review in the appropriate area.

CO2: Identify the appropriate methodology to solve the research problem.

CO3: Apply the AI tools & techniques to solve the identified problem.

CO4: Communicate scientific discoveries through peer-reviewed publications.

CO-PO Mapping

PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO

1 2 3 4 5 6 7 8 9 0 1 2 1 2 3

CO 3 3 3 3 3 2 2 2 3 3 3 3 - - 3
1

CO 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3
2

CO 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3
3

CO 3 3 3 3 3 2 2 2 3 3 3 3 - - -
4

Evaluation Pattern

Assessment Weightage (%)

Internal 70
External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 SEMESTER VIII

22AIE499 PROJECT PHASE – 2 L-T-P-C: 0- 0- 30- 10

Course Objectives
 Project Phase – 2 aims at helping students to solve the identified research problem
 The course introduces the students to real world problems associated with AI
 The course also aims at helping students to publish scientific articles in peer reviewed scientific
publications.

Course Outcomes

After completing the course, the students will be able to

CO1: Solve a valid research problem by employing appropriate tools & techniques.

CO2: Implement the appropriate methodology to solve the research problem.

CO3: Apply the AI tools & techniques to solve the identified problem.

CO4: Communicate scientific discoveries through peer-reviewed publications.

CO-PO Mapping

PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO

1 2 3 4 5 6 7 8 9 0 1 2 1 2 3

CO 3 3 3 3 3 2 2 2 3 3 3 3 - - 3
1

CO 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3
2

CO 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3
3

CO 3 3 3 3 3 2 2 2 3 3 3 3 - - -
4

Evaluation Pattern

Assessment Weightage (%)

Internal 70
External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 PROFESSIONAL ELECTIVES

22AIE431 APPLIED CRYPTOGRAPHY L-T-P-C: 2-0-3-3

Course Objectives
 This course will provide a strong grasp of the basic concepts underlying classical, modern cryptography
and its fundamentals.
 This course will help students to understand how security is defined and proven at the cryptographic
level.
 This course will help students to understand common attacks and how to prevent them.
 This course will help students to gain the ability to apply appropriate cryptographic techniques to a
security engineering (and management) problem at hand.

Course Outcomes

After completing this course, students will be able to

CO1: Implement the concepts of classical and modern cryptography.

CO2: Analyze the common attacks and the preventive systems.
CO3: Apply appropriate cryptographic techniques to a security engineering problem.
CO4: Implement canonical security protocols.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 3 3 1 1 2 3 - - 3 3 - 2 3 3 -

CO2 1 - - - - 3 - - 3 3 - - - - -

CO3 3 3 1 2 3 3 - - 3 3 - 1 3 3 -

CO4 2 - - 1 3 3 - - 3 3 - - - - -

Syllabus

Overview of cryptography - What is a cipher, Basic symmetric-key encryption- One time pad and stream ciphers,
Block ciphers, Block cipher abstractions: PRPs and PRFs, DES and Enhancements, AES, Attacks on block
ciphers, Message integrity- Message integrity: definition and applications, Collision resistant hashing,
Authenticated encryption: security against active attacks, Public key cryptography- Arithmetic modulo primes,
Cryptography using arithmetic modulo primes, Public key encryption, Arithmetic modulo composites, RSA,
Attacks on RSA, Rabin Cryptosystem, Discrete Logarithm Problem and related Algorithms, ElGamal
Cryptosystem, Introduction to Elliptic Curve Cryptography, Digital signatures: definitions and applications, More
signature schemes and applications, Identification protocols, Authenticated key exchange and SSL/TLS session
setup, Zero knowledge protocols.

Text Books / References

D. Bonesh and V Shoup, A Graduate Course in Applied Cryptography, Stanford university Press, Volume-
0.4.
Katz, Jonathan, and Yehuda Lindell. Introduction to modern cryptography. Chapman and Hall/CRC, 2014.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Katz, Jonathan, Alfred J. Menezes, Paul C. Van Oorschot, and Scott A. Vanstone. Handbook of applied
cryptography. CRC press, 1996.
Stallings, William. Cryptography and network security: principles and practice. Upper Saddle River:
Pearson, 2017.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE432 NETWORK & WIRELESS SECURITY L-T-P-C: 2- 0- 3- 3

Course Objectives
 This course covers security and privacy issues in wireless networks and systems, such as cellular
networks, wireless LANs, wireless PANs, mobile ad hoc networks, vehicular networks, satellite
networks, wireless mesh networks, sensor networks and RFID systems.
 This course will lay down the Functions, protocols and configurations for realizing authentication, key
distribution, integrity, confidentiality and anonymity in wireless access networks for mobile users.
 This course presents security techniques employed in existing systems, such as WPAN, WLAN, UMTS
and IMS.
 This course will help students to propose solutions for new network technology, such as various types of
ad-hoc networks. Digital forensics in wireless systems.

Course Outcomes

After completing this course, students will be able to

CO1: Analyze security technology and methods for communication systems that provide services for mobile
users by wireless access networks.
CO2: Analyze security mechanisms and protocols in wireless communication systems, such as the topical
technologies of WLAN IEEE 802.11, WAN 802.16, GSM/UMTS/LTE, Ad-hoc and sensor networks.
CO3: Apply design principles, mechanisms and solutions used in wireless network security to obtain
authentication and key transport protocols
CO4: Implement the security mechanisms and protocols using canonical models.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 1 2 - - - 1 - - 3 3 - 3 - - -

CO2 1 2 - - - 1 - - 3 3 - 3 - - -

CO3 2 2 2 2 2 3 - - 3 3 - 3 2 2 -

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO4 3 2 3 3 2 3 - - 3 3 - 3 2 2 -

Syllabus

Introduction to network security and wireless network, Wireless network technologies and application, Security
and Cryptography, Network Security Protocols, Security and Layered Architecture, Voice-Oriented Wireless
Networks, Data-Oriented Wireless Networks, Security in Traditional Wireless Networks, Security in Wireless
LAN, Security in Wireless Ad Hoc Network.

Text Books / References

Xiao, Yang, Hui Chen, Shuhui Yang, Yi-Bing Lin, and Ding-Zhu Du. "Wireless network security."
(2009), Springer.
Vacca, J. R, Guide to wireless network security. Springer Science & Business Media ,2006.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30s

22AIE433 INTRUSION DETECTION & PREVENTION SYSTEMS L-T-P-C: 2- 0- 3- 3

Course Objectives
 This course helps the students to understand when, where, how, and why to apply Intrusion Detection
tools and techniques in order to improve the security posture of an enterprise.
 This course helps the students to apply knowledge of the fundamentals and history of Intrusion
Detection in order to avoid common pitfalls in the creation and evaluation of new Intrusion Detection
Systems.
 This course helps the students to analyse intrusion detection alerts and logs to distinguish attack types
from false alarms.

Course Outcomes

After completing this course, students will be able to

CO1: Analyze basic issues, concepts, principles, and techniques in intrusion detection.
CO2: Analyse intrusion detection systems for particular security requirements.
CO3: Design preventive systems for various engineering applications
CO4: Implement preventive systems for various engineering applications.

CO-PO Mapping

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 2 1 3 2 3 2 2 2 3 2 1 3 - - -

CO2 2 2 3 2 3 2 2 2 3 2 1 3 - - -

CO3 2 3 3 2 3 2 2 3 3 2 1 3 2 2 3

CO4 2 3 3 2 3 2 2 3 3 2 1 3 2 2 3

Syllabus

Introduction-Understanding Intrusion Detection – Intrusion detection and prevention basics – IDS and IPS
analysis schemes, Attacks, Detection approaches –Misuse detection – anomaly detection – specification based
detection – hybrid detection , Theoretical foundations of detection-Taxonomy of anomaly detection system –
fuzzy logic – Bayes theory – Artificial Neural networks – Support vector machine – Evolutionary computation –
Association rules – Clustering, Architecture and implementation-Centralized – Distributed – Cooperative
Intrusion Detection – Tiered architecture, Justifying intrusion detection-Intrusion detection in security – Threat
Briefing –Quantifying risk – Return on Investment (ROI), Applications and tools -Tool Selection and Acquisition
Process – Introduction to various commonly used IDS and IPS Systems - Bro Intrusion Detection – Prelude
Intrusion Detection – Cisco Security IDS – Snorts Intrusion Detection – NFR security, Legal issues and
Organizations standards-Law Enforcement / Criminal Prosecutions – Standard of Due Care – Evidentiary Issues,
Organizations and Standardizations.

Text Books / References

Ali A. Ghorbani, Wei Lu, “Network Intrusion Detection and Prevention: Concepts and Techniques”, Springer,
2010.
Carl Enrolf, Eugene Schultz, Jim Mellander, “Intrusion detection and Prevention”, McGraw Hill, 2004 Paul E.
Proctor, “The Practical Intrusion Detection Handbook “, Prentice Hall, 2001.
Ankit Fadia and Mnu Zacharia, “Intrusiion Alert”, Vikas Publishing house Pvt., Ltd, 2007.
Earl Carter, Jonathan Hogue, “Intrusion Prevention Fundamentals”, Pearson Education, 2006.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE434 SOFTWARE VULNERABILITY ANALYSIS L-T-P-C: 2- 0- 3- 3

Course Objectives
 This course teaches software engineering techniques for building security into software as it is
developed.
 This course introduces students to the discipline of designing, developing, and testing secure and
dependable software-based systems.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  This course provides hands on experience in software security analysis and development using Fortify,
Threat Modelling, and Rational AppScan software.

Course Outcomes

After completing this course, students will be able to

CO1: Analyse the security risk of a system under development.
CO2: Apply secure coding practices to prevent common vulnerabilities from being injected into software.
CO3: Design security requirements (which include privacy requirements).
CO4: Validate security requirements.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 2 1 3 2 3 2 1 2 3 - - -

CO2 2 2 3 2 3 2 2 2 3 - 2 2

CO3 2 3 3 2 3 2 3 3 3 2 2 3 2 2 2

CO4 2 3 3 2 3 2 3 3 3 2 2 3 - - -

Syllabus

Introduction to software and system security principles-Confidentiality, Integrity, and Availability, Isolation,
Least Privilege, Compartmentalization, Threat Model, Bug versus Vulnerability, Secure Software Life Cycle-
Software Design, Software Implementation, Software Testing, Continuous Updates and Patches, Modern
Software Engineering, Memory and Type Safety - Pointer Capabilities, Memory Safety, Spatial Memory Safety,
Temporal Memory Safety, a Definition of Memory Safety, Practical Memory Safety, Type Safety, Défense
Strategies – Software verification, Software testing, Language-based security, Mitigations – data execution
prevention, Address space layout randomization, Stack integrity, Safe exception handling, Fortify source, Control
flow integrity, Code pointer integrity, sandboxing and software-based fault isolation, Attack vectors – Denial of
service, information Leakage, Privilege escalation, Web security- Browser security, Command injection, Sql
injection , Cross site scripting, Mobile security- Android system security, application-specific security measures.

Text Books / References

Mathias Payer, “Software Security: Principles, Policies, and Protection”, HexHive Books, edition 0.35, 2019
Anderson, Ross. Security engineering. John Wiley & Sons, 2008.
Dowd, Mark, John McDonald, and Justin Schuh. The art of software security assessment: Identifying and
preventing software vulnerabilities. Pearson Education, 2006.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 22AIE435 CYBERCRIME INVESTIGATIONS & DIGITAL FORENSICS L-T-P-C: 2- 0- 3- 3

Course Objectives
 This course provides an overview of global reach of the Internet and various cybercrimes in various
domains.
 This course provides an overview of cybercrime and the digital law enforcement practices put in place
to respond to them.
 The course will focus on the types and extent of current cyber-crimes, how the justice system responds
to these crimes, the various constitutional protections afforded to computer users, the law and policies
that govern cybercrime detection and prosecution, and related technologies.

Course Outcomes

After completing this course, students will be able to

CO1: Analyse the nature and scope of cybercrime.
CO2: Develop knowledge of major incidents of cybercrime and their resulting impact.
CO3: Analyse national and global digital law enforcement efforts
CO4: Evaluate the specific technology that facilitates cybercrime and digital law enforcement

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 2 1 2 2 3 3 3 1 3 - - -

CO2 2 2 3 2 3 3 3 2 3 2 2 2

CO3 2 3 3 2 3 3 2 3 3 2 2 3 - - -

CO4 2 3 3 2 3 3 2 3 3 2 2 3 - - -

Syllabus

Introduction to cybercrime, criminal law, courts, and law-making, Types of computer-related crimes, Sources of
cybercrime law (substantive and procedural), Technology, cybercrime, and police investigations, Technology and
crime, Cyber deviance, cybercrime, and cyber terror, Computer misuse crimes, Malware and automated computer
attacks, Malware, DDoS attacks, and Botnets, Digital piracy and Intellectual property theft, Digital piracy,
Copyright, trademark, and trade secrets, Pornography, prostitution, and sex crime, The Fourth Amendment,
computers, and computer networks, Digital/Computer Forensics -Introduction to digital and computer forensics,
Legal issues related to digital investigations, National security.

Text Books / References

Thomas J. Holt, Adam M. Bossler, and Kathryn C. Seigfried-Spellar. 2015. Cybercrime and Digital Forensics:
An Introduction. New York: Routledge. ISBN: 978-1138021303.
Nate Anderson. 2014. The Internet Police: How Crime Went Online, and the Cops Followed. New York: W.W.
Norton & Company, Inc. ISBN: 978-0393349450.
Peter Grabosky. 2016. Cybercrime. Oxford/New York: Oxford University Press. ISBN: 978-0190211554. Kevin
F. Steinmetz. 2016. Hacked: A Radical Approach to Hacker Culture and Crime. New York: New York University
Press. ISBN: 978-1479869718.
Orin S. Kerr. 2013. Computer Crime Law (3ded.). St. Paul: Thomsen Reuters. ISBN: 978-0314281364. Susan
W. Brenner. 2012. Cybercrime and the Law: Challenges, Issues, and Outcomes. Lebanon, NH: Northeastern
University Press. ISBN: 978-1555537999.
Ralph D. Clifford. 2011. Cybercrime: The Investigation, Prosecution and Defense of a Computer-related Crime.
Durham: Carolina Academic Press. ISBN: 978-1594608537.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE436 DISTRIBUTED SYSTEM SECURITY L-T-P-C: 2- 0- 3- 3

Course Objectives
 This course emphasises on the techniques for creating functional, usable, and high-performance
distributed systems.
 The course focuses on security in networks and distributed systems, and gives a short introduction to
cryptography.
 The course covers threats against distributed systems, as well as applicable methods, technologies and
standards to protect against these threats.

Course Outcomes

After completing this course, students will be able to

CO1: Analyse threats against distributed systems and the protection measures against such threats
CO2: Design secure distributed systems to evaluate the security of existing solutions.
CO3: Analyse the principles and standards of security protocols.
CO4: Implement cryptographic mechanisms to secure modern distributed systems.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO
CO1 2 1 3 2 3 2 2 3 2 1 3 - - -

CO2 2 2 3 2 3 2 2 1 3 2 1 3 2 2 2

CO3 2 3 3 2 3 2 2 1 3 2 1 3 - - -

CO4 2 3 3 2 3 2 2 1 3 2 1 3 2 2 2

Syllabus

Understanding the Core Concepts of Distributed Systems -distributed systems designs, system constraints, trade-
offs and techniques in distributed systems, distributed system for different data and applications, Distributed
system security-Access and location transparency, Processes and Communication, naming, Parallelization of tasks
- Concurrency and Synchronization, Consistency and Replication, Distributed system Security and network
protocols – types of attacks, encryption algorithms, authentication, public key cryptosystems, data verification.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Text Books / References
Andrew S. Tannenbaum and Maarten Van Steen, “Distributed Systems: Principles and Paradigms”, Second
Edition, Pearson, 2007.
Belapurkar, Abhijit, Anirban Chakrabarti, HarigopalPonnapalli, Niranjan Varadarajan, Srinivas
Padmanabhuni, and Srikanth Sundarrajan. Distributed systems security: issues, processes and solutions. John
Wiley & Sons, 2009.
George Coulouris, Jean Dollimore, Tim Kindberg, and Gordon Blair, “Distributed Systems: Concepts and
Design”, Fifth Edition, Addison Wesley, 2011.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE437 MEDICAL IMAGE PROCESSING L-T-P-C: 2- 0- 3- 3

Course Objectives:

 To provide the basics of different medical image modalities.

 To introduce the basic concepts applied in medical image processing.
 To introduce different machine learning/deep learning-based algorithms for medical image analysis.
 To apply tools and methodologies for medical image processing and analysis.

Course Outcomes
After completing this course, students will be able to:

CO1: Apply basic image processing techniques in medical data.

CO2: Implement image enhancement algorithms in medical data.

CO3: Implement image segmentation algorithms in medical data.

CO4: Apply machine learning/deep learning algorithms for medical image analysis.

CO-PO Mapping

PO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO

CO1 3 2 2 - 3 2 - - 3 3 - 3 2 3 2

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO2 3 2 2 2 3 3 - - 3 3 2 3 3 3 2

CO3 3 2 2 2 3 3 - - 3 3 2 3 3 3 2

CO4 3 3 2 2 3 3 - - 3 3 2 3 3 3 3

Syllabus:

Unit 1

Imaging Modalities: Survey of major modalities for medical imaging: Ultrasound, X-ray, CT, MRI, PET, and
SPECT.

Unit 2

Image Processing and Analysis: Registration, Feature Extraction: Edge Detection, Hough transform, Filtering:
Noise removal and Image Enhancement, Segmentation, Domain transformation.

Unit 3
Introduction to Machine Learning/Deep Learning Approaches for Biomedical Image Classification, Biomedical
Image Segmentation, Case studies on some recent advances in analysis of retinal, CT, MRI, ultrasound and
histology images.

Textbooks/ References:

Sinha G. R, Patel, B. C., “Medical Image Processing: Concepts and Applications”, Prentice Hall, 2014.

Gonzalez R C, Woods R E, “Digital Image Processing”, Third Edition, Prentice Hall, 2007.

Rangayyan R M, “Biomedical Image Analysis”, Fifth Edition, CRC Press, 2005.

Evaluation Pattern:

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 22AIE438 BIOMEDICAL SIGNAL PROCESSING L-T-P-C: 2- 0- 3- 3

Course Objectives
The objectives of this course are:
 To provide the basics of different types of biomedical signals.
 To introduce the basic concepts of time domain and frequency domain analysis in biomedical signals.
 To introduce machine learning/deep learning-based algorithms for biomedical signal analysis.
 To impart skills to develop efficient deep learning models on biomedical data.

Course Outcomes
After completing this course, students will be able to:

CO1: Apply basic time domain processing techniques in biomedical signals.

CO2: Implement frequency domain transformation algorithms in biomedical signals.

CO3: Implement decomposition-based filtering techniques in biomedical signals.

CO4: Apply machine learning/deep learning algorithms for biomedical signal analysis.

CO-PO Mapping

PO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO

CO1 3 2 2 - 3 2 - - 3 3 - 3 2 3 2

CO2 3 2 2 2 3 3 - - 3 3 2 3 3 3 2

CO3 3 2 2 2 3 3 - - 3 3 2 3 3 3 2

CO4 3 3 2 2 3 3 - - 3 3 2 3 3 3 3

Syllabus:

Unit 1
Introduction to Biomedical Signals: Action Potential and Its Generation, Origin and Waveform Characteristics of
Basic Biomedical Signals Like: Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram
(EMG), Phonocardiogram (PCG), Electroneurogram (ENG), Event-Related Potentials (ERPS),
Electrogastrogram (EGG), Objectives of Biomedical Signal Analysis, Difficulties in Biomedical Signal Analysis,
Computer-Aided Diagnosis.

Unit 2

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Biosignal Analysis: Time-domain analysis of Biosignals, Fourier Spectrum of Biosignals, Short Time Fourier
Transform and Spectrogram, Discrete Cosine Transform and its Applications, Signal Decomposition based
filtering: Wavelet Transform, Hilbert Transform, Empirical Mode Decomposition and Empirical Wavelet
Transform.

Unit 3
Introduction to Machine Learning/Deep Learning Approaches for Biomedical Signal Detection and Classification.
Performance Measures for Detection and Classification System. Case studies on some recent advances in analysis
of biomedical signals.

Textbooks/ References:
Rangayyan R M, “Biomedical Signal Analysis: A case-study approach”, Wiley India, 2009.
Eugene N. Bruce, “Biomedical Signal Processing and Signal Modeling”, Wiley Inter-Science, 1st edition, 2000.
John.L.Semmlow, “Biosignal and Biomedical Image Processing: Matlab-based applications”, CRC, 1st edition,
2004.
Stephen Mallet, “A Wavelet Tour of Signal Processing: The Sparse Way”, 3rd edition, Academic Press, 2008.

Evaluation Pattern:

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE439 CLINICAL INFORMATION SYSTEM L-T-P-C: 2- 0- 3- 3

Course Objectives

 To gain insights and situational experience with clinical information systems.

 To examine the effective use of data and information technology to assist the students to easily migrate
from paper-based systems to CIS, thereby improving organizational performance.
 To understand the impacts of CIS on patients and health care providers.

Course Outcomes
After completing this course, students will be able to:

CO1: Explore the basics of clinical information systems.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO2: Apply information technology and related tools in workflow design.

CO3: Explore the “benefits and barriers” associated with electronic health records.

CO4: Apply strategies for minimizing the major barriers to the adoption of electronic health records.

CO-PO Mapping

PO
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO

CO1 2 2 1 1 3 2 - - 3 3 1 3 - 3 -

CO2 3 3 3 3 3 2 - 1 3 3 3 3 1 3 3

CO3 3 3 1 1 3 2 - - 3 3 1 3 - 3 -

CO4 3 3 3 3 3 2 - 1 3 3 3 3 2 3 3

Syllabus:

Unit 1
Introduction to clinical information systems – contemporary issues in healthcare – workflow and related tools for
workflow design – electronic health records databases – Healthcare IT & portable technology.

Unit 2
Data mining in health care, Artificial intelligence in health care: Use of AI, The healthcare industry, Electronic
medical records, Clinical decision support systems.

Unit 3
Bioethics and challenges to deployment, Challenges in clinical decision support.

Textbooks/ References:
Sittig & Ash, Clinical Information Systems – Overcoming Adverse Consequences, Jones & Bartlett Learning
Publishers, 2009.

Edward H. Shortliffe; Leslie E. Perreault, Medical Informatics – Computer Applications in Healthcare and
Biomedicine, Springer-Verlag New York Inc. Publishers, 2014.

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE440 KINEMATICS & KINETICS FOR ROBOTICS L-T-P-C: 2-0-3-3

Course Objectives

 To introduce the basic concepts of Kinetics & Kinematics of robotic systems and investigate the
connections between Kinetics and Kinematics of robotic systems.
 The course will introduce the state-of-the-art computational tools to solve the Kinetics and Kinematics
problems

Course Outcome
After completing this course, the students will be able to

CO1: Understand the fundamentals of Kinematics & Kinetics for Robotics.

CO2: Apply the concepts of vector mechanics for solving Kinematics problems.

CO3: Apply computational techniques to solve Kinematics & Kinetics problems.

CO4: Implement computational models for Kinematics & Kinetics problems.

CO-PO Mapping

PO/PS PSO
O P P P P P P P P P PO PO PO PS PS 3
CO O O O O O5 O O O O 10 11 12 O1 O2
1 2 3 4 6 7 8 9
CO1 3 2 2 2 2 1 3 2 3 3 3
CO2 3 3 2 2 2 1 3 2 3 3 3
CO3 3 3 3 3 3 2 3 2 3 3 3 2
CO4 3 3 3 3 3 2 3 2 3 3 2 3

Syllabus
Components and Mechanisms of a Robotic System – Link – Joint – Manipulator – Actuator – Sensor – Controller
– Kinetics and Kinematics of Robots – Rotation Kinematics – Rotation about Global and Local Axes – Euler
angles – Transformation Matrices – Rotation Matrix – Quaternion – Composition and decomposition of Rotations
- Homogeneous transformation – Inverse Homogeneous transformation – Compound homogeneous
transformation – Forward Kinematics – D-H Notation – Inverse Kinematics – Angular Velocity – Velocity
Kinematics – Numerical Methods in Kinematics.

Textbooks/References
Theory of Applied Robotics: Kinematics, Dynamics & Control – R. Jazar, Springer, 2010.

Statics and Kinematics with application to Robotics: J. Duffy, Cambridge University Press, 1996.

Kinematics and Dynamics of Machinery – Wilson & Sadler, Third Edition, Pearson Publication, 2003.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE441 DYNAMICS & CONTROL OF ROBOTICS L-T-P-C: 2-0-3-3

Course Objectives

 To provide a mathematical foundation to dynamics and control of robotic systems and introduce a set of
analytical and computational tools for the modelling and control of robots.
 This will enable the students to simulate and control robotic motion for various types of robotic systems.

Course Outcome
After completing this course, the students will be able to

CO1: Develop mathematical models for dynamics and control of robotic systems.

CO2: Apply analytical and computational tools for modelling and control of robots.

CO3: Simulate simple robotic motion.

CO4: Control simple robotic motion.

CO-PO Mapping

PO/PS PSO
O P P P P P P P P P PO PO PO PS PS 3
CO O O O O4 O5 O6 O7 O8 O9 10 11 12 O1 O2
1 2 3
CO1 2 3 2 3 2 1 - - 3 3 3 3 3 - -
CO2 3 3 3 3 3 1 - - 3 3 3 3 2 3 2
CO3 3 2 3 3 3 2 - - 3 3 3 3 - 3 3
CO4 3 2 3 3 3 2 - - 3 3 3 3 - 3 3

Syllabus
Dynamics of Robotics – Acceleration Kinematics – Motion Dynamics – Review of Rigid body Kinetics –
Translational Kinetics – Rotational Kinetics – Rigid link acceleration – Newton-Euler dynamics – Recursive
Newton – Euler Dynamics – Lagrange Equations – Robot Statics – Introduction to control of robotics – Path
Planning – Polynomial Path – Non-Polynomial Path – Cartesian Path – Rotational Path – Manipulator Motion –
Time optimal control – Bang – Bang control – Open Loop and Closed Control – Classical Control Techniques –
Modern Control Techniques – Sensing and Control.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Textbooks / References

Theory of Applied Robotics: Kinematics, Dynamics & Control – R. Jazar, Springer, 2010.

Advances in Robotics, automation and control: Aramburo& Trevino, In-Tech Publishers, 2008.
Robotics: Modelling, Planning & Control- B Siciliano, L Sciavicco, L Villani & G Oriolo. Springer Text books in
Control and Signal Processing, 2009.

Aspects of Soft Computing, Intelligent Robotics and Control –Janos Fodor – Springer Publishers, 2009.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE442 ROBOTIC OPERATING SYSTEMS & ROBOT SIMULATION L-T-P-C: 2- 0- 3- 3

Course Objectives

 To provide an introductory understanding on robotic operating system and gazebo simulation

environment.
 To introduce the students with module developments in ROS for mobile robot control, navigation and
environment mapping.
 To introduce the students with module developments in ROS for industrial robot control, path planning
and trajectory planning.

Course Outcomes
After completing this course student will be able to,

CO1: Apply the principles of ROS for module development of robotic systems.

CO2: Analyse various robotic systems using ROS integrated simulation platforms.
CO3: Apply the knowledge of robotic system and ROS for mobile robot control, navigation and environment
mapping using ROS simulators.

CO4: Develop prototypical robotic systems using ROS for real-time problems.

CO-PO Mapping

PO/ PO PO PO PO PO5 PO PO PO PO PO1 PO1 PO1 PSO PS PS

PSO 1 2 3 4 6 7 8 9 0 1 2 1 O2 O3
CO
CO1 1 2 1 1 3 2 - - 3 3 - 2 2 3 2
CO2 3 2 1 2 3 2 - - 3 3 - - 2 3 2
CO3 3 2 3 2 3 - 3 3 3 3 3 2 2 3 2

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO4 3 2 3 2 3 - 3 3 3 3 3 2 2 3 2

Syllabus
ROS concepts - Preliminaries – Publishing a topic – Subscribing to a topic – Latched topics – Defining message
types – Mixing Publishers and subscribers – Services – Defining a service – Implementing a service – Using a
service – Actions – Definition of an Action – Implementing a basic action server – Robots model and Simulators
– Sub systems – Actuation: Mobile platform – Actuation manipulator arm – Cameras and Scanners

Text Book /Reference Books

Joseph, Lentin, and Jonathan Cacace. Mastering ROS for Robotics Programming: Design, build, and simulate
complex robots using the Robot Operating System. Packt Publishing Ltd, 2018.

Programing Robots with ROS’, M. Quigley, B. Gerkey, and W. D. Smart, Oreilly Publishers, 2015.

Koubâa, Anis, ed. Robot Operating System (ROS). Vol. 1. Cham: Springer, 2017.

‘ROS Robotics by example’, Fairchild & Harman, PACKT Publishing, 2016

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE443 Underactuated Robotics L-T-P-C: 2- 0- 3- 3

Course Objectives

 This course covers nonlinear dynamical aspects and control of mechanical systems that are
underactuated, with a focus on computational approaches.
 The course helps in establishing the understanding of nonlinear dynamics of robotic manipulators,
applied optimal and robust control and motion planning
 The course aims to discuss examples from biology and applications to legged locomotion, compliant
manipulation, underwater robots, and flying machines.
Course Outcomes
After completing this course, students will be able to:

CO1: Analyze nonlinear underactuated systems

CO2: Demonstrate simple robot models for walking and running

CO3: Simulate the dynamics and control of Highly articulated robots

CO4: Perform nonlinear planning and control of simple robot models.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO-PO Mapping

PO P PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO

O 2 3 4 5 6 7 8 9 0 1 2 1 2 3
CO 1
CO1 3 3 2 1 3 - 3 3 2 3 - 2

CO2 3 3 1 1 3 1 3 3 1 2 1 3

CO3 3 3 3 2 3 1 3 3 2 3 2 3

CO4 3 3 3 2 3 - 3 3 2 3 1 3

Syllabus
Underactuated systems – Introduction, Nonlinear modeling – Simple pendulum, Nonlinear analysis of
complicated systems – Acrobots - Cart-poles – Quadrotores – Pendubot - Inertia wheel pendulum - Furuta
pendulum (horizontal rotation and vertical pendulum) – Hovercraft, Models for – Walking – Running – Walking
and Running, Highly-articulated Legged Robots, Model Systems with Stochasticity, Nonlinear Planning and
Control – Dynamic programming, Linear Quadratic Regulators, Lyapunov Analysis, Trajectory Optimization,
Policy Search, Motion Planning as Search, Feedback Motion Planning, Robust and Stochastic Control, Output
Feedback, Algorithms for Limit Cycles, Planning and Control through Contact, Estimation and Learning - System
Identification, State Estimation, Model-Free Policy Search

Textbooks:
Anthony Bloch and P. Crouch and J. Baillieul and J. Marsden, "Nonholonomic Mechanics and Control",
Springer, April 8, 2003.
Strogatz, Steven H. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and
Engineering. Boulder, CO: Westview Press, 2001. ISBN: 9780738204536.
Fantoni, Isabelle, and Rogelio Lozano. Non-linear Control for Underactuated Mechanical Systems. New York,
NY: Springer-Verlag, 2002. ISBN: 9781852334239.
Bertsekas, Dimitri P. Dynamic Programming and Optimal Control. 3rd ed. Vols. I and II. Nashua, NH: Athena
Scientific, 2007. ISBN: 9781886529083 (set).
LaValle, Steven M. Planning Algorithms. New York, NY: Cambridge University Press, 2006. ISBN:
9780521862059.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 22AIE444 PROBABILISTIC ROBOTICS L-T-P-C: 2- 0- 3- 3

Course Objectives

 The course aims at statistical techniques for representing information and making decisions in robotics.
 The course helps to overcome the uncertainty that arises in most contemporary robotics applications.

Course Outcomes
After completing this course, students will be able to:

CO1: Enumerate the fundamental aspects concerning mobile robotics.

CO2: Apply state estimation techniques and observability filters to mobile robots.

CO3: Apply simultaneous localization and mapping and its variations for mobile robot path planning.

CO4: Analyze the decision-making process for mobile robots.

CO-PO Mapping
PO P PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO
O 2 3 4 5 6 7 8 9 0 1 2 1 2 3
CO 1
CO1 3 - - - 3 - - - 3 3 - 2 3 - 2

CO2 3 2 2 2 3 - - - 3 3 - 2 3 - 3

CO3 3 2 2 2 3 1 2 2 3 3 - 2 3 2 3

CO4 3 3 3 3 3 2 - 2 3 3 - 2 3 - 3

Syllabus
Introduction & Robot Paradigms, State Estimation, Gaussian Filters - Kalman Filter - Extended Kalman
Filters & Geometric Approach, Nonparametric Filters - Discrete and Particle Filters, Wheeled
Locomotion & Robot Motion Models, Sensors & Robot Perception Models, Mapping with known poses, SLAM
- The FastSLAM Algorithm - GraphSLAM - Self SLAM, Exploration and 3D Mapping, Uncertain knowledge
and reasoning - Probabilistic Reasoning - Probabilistic Reasoning over Time - Making Simple Decisions -
Making Complex Decisions -Multiagent Decision Making – Robotics.

Text Books / Reference Books

Sebastian Thrun, Wolfram Burgard and Dieter Fox, Probabilistic Robotics, The MIT Press, 2005. ISBN:
9780262201629, 3rd edition.
Stuart Russell and Peter Norvig 'Artificial Intelligence - A Modern Approach' 3rd edition.
Machine Learning: A Probabilistic Perspective, Kevin Patrick Murphy.
MIT Press, 2012.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE445 SENSORS AND ACTUATORS FOR ROBOTICS L-T-P-C: 2- 0- 3- 3

Course Objectives

 The course aims to give a reasonable understanding of the principles and operations of sensors and
actuators for robotics
 The course helps with the selection of sensors and actuators for the robot based on the application.
Course Outcomes
After completing this course, students will be able to:

CO1: Distinguish the different classes of sensors and actuators suitable for robotics application

CO2: Analyze the principle of operation of different sensors and actuators used in robotics application

CO3: Design sensors and actuators for robotics applications with easy implementation and cost-effectiveness.
CO4: Identify the best sensor and actuator for accomplishing the work with accuracy, convenient operating
features, and great functionality.

CO-PO Mapping

PO P PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO

O 2 3 4 5 6 7 8 9 0 1 2 1 2 3
CO 1
CO1 3 1 1 - 3 - - - 3 3 - 1 1 - 3

CO2 3 1 1 - 3 - - - 3 3 - 1 1 - 3

CO3 3 2 3 1 3 2 1 1 3 3 - 1 1 - 3

CO4 3 3 3 2 3 2 1 1 3 3 - 1 1 - 3

Syllabus
Sensors for robots: Sensor classification and characteristics, Touch and proximity sensors: IR, Photodiodes.
Tactile sensors, collision sensors, interaction sensors – proximity/distance sensors, Position measurement: Optical
encoder, Potentiometer, 2D and 3D cameras, Velocity measurement. Inertial sensors: Gyroscopes, Accelerometer.
Force sensors, Torque sensors. Range sensors: IR, Ultrasonic sensors, laser ranger finder. Robot actuators:
Hydraulic actuators, Pneumatic Actuator, Electrical actuator, Introduction to motors: DC motors, AC motors,
Stepping motors, Servo motors. Motion transmission: Gear transmission, Belt transmission. Harmonic drive.

Text Books / References

Sensors, Actuators, and Their Interfaces: A multidisciplinary introduction, 2nd edition. Nathan Ida, 2020.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Industrial Robotics: Technology Programming and Applications, 2nd Edn, Mikell P Groover, Tata McGraw Hill
Education Private Limited, 2012.
John J Craig, Introduction to Robotics, Mechanics and control, second edition Addison – Wesley, 1999.
Robert J Schilling: Fundamentals of Robotics, Analysis and Control. Prentice Hall of India, 1996.
http://www.societyofrobots.com/robot_tutorial.sh tml#sensors
http://www.sensorcentral.com/photoelectric/ultra sonic01.php

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE446 NLP FOR ROBOTICS L-T-P-C: 2- 0- 3- 3

Course Objectives

 The course aims to introduce spoken language technology with an emphasis on dialog and
conversational systems
 The course helps in establishing the understanding of Deep learning and other methods for automatic
speech recognition, speech synthesis systems for robotics
Course Outcomes
After completing this course, students will be able to:

CO1: Apply the basics of speech and language processing for robotics.

CO2: Build Dialog systems using the NLP pipeline for robotics.

CO3: Implement different end-to-end deep neural network approaches for speech recognition.

CO4: Build text to speech systems for dialogue systems

CO-PO Mapping

PO P PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO

O 2 3 4 5 6 7 8 9 0 1 2 1 2 3
CO 1
CO1 3 2 2 2 2 2 - - 2 2 1 2 1 - 3

CO2 3 3 3 3 3 3 - - 3 3 3 3 1 3 3

CO3 3 3 3 3 3 3 - -- 3 3 3 3 1 3 3

CO4 3 3 3 3 3 3 - - 3 3 3 3 1 3 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Syllabus
Introduction and Acoustic Phonetics, Overview of dialog: Human conversation. Task-oriented dialog. Dialog
systems, Machine Learning in Dialog- Recurrent NNs, Attention, Transformers, Automatic Speech Recognition,
Foundation models for spoken language-Using the Speech Brain ASR toolkit, Advanced ASR, Text to Speech
(TTS): Overview. Text normalization, Spectrogram prediction, Vocoding, TTS Evaluation.

Text Books / Reference Books

Dan Jurafsky and James H. Martin. Speech and Language Processing, (3rd ed. draft), available at
https://web.stanford.edu/~jurafsky/slp3/
Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing. Available at
https://u.cs.biu.ac.il/~yogo/nnlp.pdf
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press. Available at
https://www.deeplearningbook.org/

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE447 DATA DRIVEN CONTROL IN ROBOTICS L-T-P-C: 2- 0- 3- 3

Course Objectives
1. The course aims to review the basic modelling and control aspects of robotic systems.
2. The course then directs to data-based methods for better control of robotic systems.
3. The course also covers the computer vision part essential for data-based control of robotics.
4. The course also imparts knowledge about learning based control systems.
Course Outcomes

After completing this course, students will be able to:

CO1: Apply principles of computer vision and machine learning for robotic control

CO2: Model dynamical robot systems using data driven techniques.

CO3: Apply machine learning techniques to build more robust robotic systems

CO4: Apply neural networks to do overall control of mobile robots.

CO-PO Mapping

PO

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO P
PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO
O
1 3 4 5 6 7 8 9 0 1 2 1 2 3
2
CO 3 2 1 - 3 - - - 3 3 - 3 2 2 3
1
CO 3 3 3 3 3 - 2 1 3 3 - 3 - 2 3
2
CO 3 3 3 3 3 2 2 1 3 3 - 3 - 2 3
3
CO 3 3 3 3 3 2 2 1 3 3 - 3 - 2 3
4

Syllabus
System Modeling - Control System Principles - Computing, Measurement, State, and Parameter Estimation -
Decision-Making and Machine Learning - Numerical Methods for Evaluation and Search - Expert Systems -
Neural Networks for Classification and Control - Vision for Robots: Mid-Level Visual State Estimation,
Direct Perception, Active and Interactive Perception, Self-Supervised Image Representations: Unstructured
Full-Scene Representations, Object and Key point - Structured Representations. Learning - Based Control:
Predictive Models and Forward Dynamics Models, Model-Based Reinforcement Learning and Visual
Servoing, Model-Free Reinforcement Learning and Sim-to-Real Transfer, Learning from Demonstrations.

Text Books / Reference Books

H. Asada and J.-J. Slotine, Robot Analysis and Control, J. Wiley & Sons, 1986.
M. Brady, J. Hollerbach, T. Johnson, T. Lozano-Perez, and M. Mason, Robot Motion: Planning and Control,
MIT Press, 1984.
P. Corke, Robotics, Vision, and Control, Springer, 2011.
A. Staugaard, Jr., Robotics and AI: An Introduction to Applied Machine Intelligence, Prentice-Hall, 1987.
P. Antsaklis and K. Passino, An Introduction to Intelligent and Autonomous Control, Kluwer, 1993.
D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
D. Kortenkamp, R. Bonasso, and R. Murphy, ed., Artificial Intelligence and Mobile Robots, AAAI Press, 1998.
K. P. Valavanis and G. N. Saridis, Intelligent Robotic Systems: Theory, Design, and Applications, Kluwer,
1992.
P. Winston and R. Brown, Artificial Intelligence: An MIT Perspective, MIT Press, 1979.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 22AIE448 INTRODUCTION TO DRONES L-T-P-C: 2- 0- 3- 3

Course Objectives

 The main aim of this course is to understand the basics of Unmanned Arial Vehicles (Drones) and its
various applications in the age of artificial intelligence.

 The course will take the students to understand the basic dynamics of drone based flying system.

 The course will provide the knowledge of basic electronic components and their working principles in a
drone/ Unmanned Aerial vehicle system

 The course will also impart the knowledge of how to fly a drone by considering the rules and regulations
to the specific country.

After completing this course, the students will be able to

CO1: Distinguish the right drone / UAV flying regulations specific to India

CO2: Analyse the working principles of various electronic components to build the drone

CO3: Apply the concept of drone dynamics and different movements during flight

CO4: Illustrate UAV flying in the given environment

CO-PO Mapping

PO /PSO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO 1 PSO PSO
2 3
CO

CO1 1 2 2 - 3 3 2 3 - 2 1 2 - - -

CO2 3 1 2 1 3 - 2 2 2 3 2 1 1 -

CO3 3 3 2 1 3 2 3 3 2 2 3 2 1 1 -

CO4 3 3 3 1 3 3 3 3 2 1 - 2 1 1 -

Syllabus
Introductions to drones and its applications in the age of AI, Drone regulations specific to India, Basics of drone
dynamics for flying - frame types, propellers, types of drones, dynamics specific to quadcopter, Understanding
UAV movements (Quadcopter), How to fly a drone, Introduction to drone electronic components, working
principle behind each electronic component, Drone and electronic assembly, flying experiments.

Textbook / References
Syed Omar Faruk Towaha, Building Smart Drones with ESP8266 and Arduino: Build exciting drones by
leveraging the capabilities of Arduino and ESP8266, Packt Publishing, 2018.
Barnhart, R. Kurt, Douglas M. Marshall, and Eric Shappee, eds. Introduction to unmanned aircraft systems. Crc
Press, 2021.

Garg, P. K. Unmanned Aerial Vehicles: An Introduction. Stylus Publishing, LLC, 2021

Kimon P. Valavanis, Handbook of Unmanned Aerial Vehicles, Volume4, Springer Netherlands, 2014.

Evaluation Pattern

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE449 INTRODUCTION TO DIGITAL MANUFACTURING L-T-P-C: 2- 0- 3- 3

Course Objectives

 This course will at imparting the knowledge of basics of digital manufacturing and its importance in
current era.

 It will also equip the students to understand about the basics of Additive manufacturing used in various
industry applications.

 Further it will expose the students to additive manufacturing technology using 3-D printing.

Course Outcomes

After completing this course, the students will be able to

CO1: Assemble and use a 3D printer.

CO2: Design simple 3D design using CAD packages.

CO3: Illustrate Slicing and evaluate the model in a CAD packages.

CO4: Design small robots and DIY projects comprising of 3D printed parts.

CO-PO Mapping

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

CO1 2 1 3 2 3 2 2 - 3 2 3 3 1 1 -

CO2 2 2 3 2 3 2 2 - 3 2 3 3 1 1 -

CO3 3 2 3 2 3 1 1 - 2 2 3 3 - 1 -

CO4 2 3 3 2 3 2 2 - 3 2 3 3 - 1 -

Syllabus
History of Manufacturing: From classical to Additive manufacturing, 3D Printers and Printable Materials, 3D
Printer Workflow and Software, selecting a printer: Comparing Technologies, working with a 3D Printer, 3D
Models, Applications, Building Projects

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Textbook/References:

Joan Horvath, Rich Cameron, Mastering 3D Printing in the Classroom, Library and Lab, Apress, 2018.

https://ultimaker.com/en/resources/education/3d-printing-in-the-classroom

Brian Evans, Practical 3d Printers the Science and Art of 3d Printing, Apress, 2018.

Chris Anderson, Makers-The New Industrial Revolution, Crown Publishing, 2018.

Kalani Kirk Hausman and Richard Horne 3D Printing for Dummies, Wiley Publications, 2018.
Ben Redwood, Filemon Schoffer, Brian Garret, 3D Printing Handbook, Technologies design and Applications,
3D Hubs, 2018.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE450 SPEECH PROCESSING L-T-P-C: 2- 0- 3- 3

Course Objective

 The objective of the course is to understand acoustic theory behind the human speech production
systems.

 As a part of this course students will be able to analyze time and frequency domain features from a speech
signal.

 Further student will be able to implement ML/DL based models for speech technology applications.

Course Outcomes

After completing this course, students will be able to

CO1: Analyse the acoustics behind of the production of a speech signal

CO2: Differentiate the characteristics of different speech sounds

CO3: Analyse the time-domain and frequency domain features of the speech signal

CO4: Implement various ML/DL approaches for modelling speech towards applications such as classification,
detection, and recognition

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO

CO1 2 2 - 2 2 --- --- --- 1 2 1 2 1 1 -

CO2 2 2 - 2 2 --- --- ---- 3 2 2 2 2 2 1

CO3 2 2 1 2 3 --- --- --- 3 2 2 2 2 2 2

CO4 3 3 2 3 3 -- --- --- 3 3 3 3 3 3 3

Syllabus

Overview of Speech Processing Systems, Speech Production and Perception, Speech Signal Characteristics,
Properties of speech sounds-Vowels and Consonants. Short time processing of speech- Time Domain parameters,
Frequency domain parameters, Spectrograms, Cepstral Analysis, Mel-frequency Cepstral Coefficients, Linear
Prediction Analysis - Speech Recognition- GMM-HMM, Machine learning and Deep neural network models used
for speech modelling and classification, Speech synthesis, End-to-End Models for speech technology
applications.

Textbooks / References

‘Fundamentals of Speech Recognition’, L. Rabiner, Biing-Hwang Juang and B. Yegnanarayana, Pearson

Education Inc.2009

‘Speech Communication’, Douglas O'Shaughnessy, University Press, 2001

‘Discrete Time Speech Signal Processing’, Thomas F Quatieri, Pearson Education Inc., 2004
Hannun, Awni, et al. "Deep speech: Scaling up end-to-end speech recognition." arXiv preprint arXiv:1412.5567
(2014).
Collobert, Ronan, Christian Puhrsch, and Gabriel Synnaeve. "Wav2letter: an end-to-end convnet-based speech
recognition system." arXiv preprint arXiv:1609.03193 (2016).

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE451 MODERN AND SMART MATERIALS L-T-P-C: 2- 0- 3- 3

Course Objectives

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

  The course aims introduce current trends in materials for innovative solutions.

 The course helps in establishing the properties of modern and smart materials involved in innovative
technologies.

 The course will augment the knowledge of Computational material science by considering the modelling
and simulation of modern and smart materials.

Course Outcomes

After completing this course, students will be able to:

CO1: Identify modern and smart materials for innovative solutions.

CO2: Distinguish important properties of modern and smart materials.

CO3: Simulate modern and smart materials using various approaches in computational material science.

CO4: Analyse the simulated modern and smart materials.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

CO

CO1 2 2 - - - - - -- 3 3 - 2 - - 1

CO2 2 2 - -- 1 - 1 - 3 3 - 2 - - 1

CO3 2 3 3 2 3 - 1 - 3 3 - 2 1 1 1

CO4 2 2 3 2 3 - 1 - 3 3 - 2 1 1 1

Syllabus
Introduction to Smart materials, Piezoelectric materials, Magenetostrictive materials, Electroactive Polymers,
Chromogenic materials, Shape Memory Alloys, Heat Energy Storage materials, Electo and Magneto Rheological
Fluids, Smart hydrogels and Smart Polymers. Smart materials for 4D printing. Modelling and Simulation of Smart
Materials. Introduction to Nanomaterials, Nanomaterial structure, Energy at Nanoscale, Functional
Nanomaterials: metal nanoparticles, quantum dots, nanoclusters, carbon-based nanomaterials, organic, inorganic,
hybrid nanomaterials, biomimetic nanomaterials, Modelling and simulation of Nanomaterials – Atomistic and
Quantum methods.

Text Books / Reference Books

‘Engineering Analysis of Smart Material Systems’, D.J. Leo, Wiley 2007.

‘Smart Structures Physical Behaviour, mathematical Modelling and Applications’ Paolo Gaudenzi, Wilet, 2009.

‘Nanoscale Materials in Chemistry’, Kenneth J. Klabunde, Ryan M. Richards, Wiley, 2009.

‘Nano: The Essentials”, T. Pradeep, McGraw-Hill (India) Pvt Limited, 2008.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE452 DATA DRIVEN MATERIAL MODELLING AND SIMULATION L-T-P-C: 2- 0- 3- 3

Course Objectives

 The course aims to review the artificial intelligence concepts relevant to computational material science.

 The course focuses on using data driven modelling in order to solve various problems in computational
material science.

 The course aims to apply the combination of artificial intelligence and material modelling to solve real
systems through data-based simulations.

 The course also helps student analyse the data driven simulations and arrive at appropriate conclusions.

Course Outcomes

After completing this course, students will be able to:

CO1: Distinguish the artificial intelligence concepts applied to material science.

CO2: Apply various algorithms pertaining to machine learning to solve real-world material science problems.

CO3: Apply various algorithms pertaining to neural networks to solve real-world material science problems.

CO4: Analyse the data driven models to arrive at solutions to real-world problems in material science.

CO-PO Mapping

PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

CO

CO1 3 1 1 - 3 - - - 3 3 - 2 2 2 2

CO2 3 3 1 1 3 - - - 3 3 - 2 2 2 2

CO3 3 3 1 1 3 - - - 3 3 - 2 2 2 3

CO4 3 3 3 3 3 - 1 - 3 3 - 2 2 2 3

Syllabus

Machine learning – Regression, Classification and Kernel Learning, Deep learning Fundamentals – Common
Neural Networks architectures, Explaining Predictions, Application of Machine learning and Neural networks in
materials science – Unsupervised learning of material spaces, Kernel Ridge Regression for materials property
Prediction, Deep learning for sequences, Predicting DFT energies with GNN, Gaussian Approximation Potentials

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

and machine learning of force field, cmlkit – Toolkit for machine learning for Material science and Quantum
Chemistry.

Text Books / Reference

‘Deep Learning for Molecules and Materials’, Andre white, [online], https://dmol.pub/intro.html.

‘Machine learning in materials science: Recent progress and emerging applications,’ Kusne, A., Mueller, T. and
Ramprasad, R., Reviews in Computational Chemistry (2016),
[online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=915933.
‘Machine learning for quantum mechanics in a nutshell’, Rupp, M. International Journal in Quantum Chemistry,
2015, 115, 1058– 1073. DOI:10.1002/qua.24954.

Nomad Tutorials . https://nomad-lab.eu/prod/v1/gui/analyze/tutorials

Recent Publications for AI in Material Science. https://archive.materialscloud.org/

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE453 COMPUTATIONAL DRUG DESIGN L-T-P-C: 2-0-3-3

Course Objectives

 The main objective of this course is to explore computer assisted drug design.

 The course focus on pharmacopore mapping associated with combinatorial chemistry.

Course Outcome

After completing this course, the students will be able to

CO1: Analyse the molecular modelling and computational formats for representing Chemicals.
CO2: Evaluate the open-source tools available for computer assisted drug design.
CO3: Analyse databases available for lead molecules and understand the developmental process.
CO4: create automated pipelines for computer assisted drug design.

CO-PO Mapping

PO/PSO

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3
CO1 3 3 3 3 3 2 3 3 3 3 2 3
CO2 3 3 3 3 3 2 3 3 3 3 3 2
CO3 3 3 3 3 3 2 3 3 3 3 3 2
CO4 3 3 3 3 3 2 3 3 3 3 2 3

Syllabus

Unit 1

Introduction to Cheminformatics, ADME Database, Chemical, Biochemical and Pharmaceutical Databases. Drug
Design and Discovery – Target Identification & validation of lead molecules – Optimisation of Virtual Screening
Technique- Drug likeness screening.

Unit 2

Molecular Modelling – Molecular Docking – Denovo Ligand Design & Structure based methods-Concept of
pharmacophore mapping and pharmacophore-based Screening – Molecular Docking – Rigid Docking- flexible
docking – manual docking – docking based screening – Informatics & Methods in Drug Design.

Textbooks / References

Kerns, E.H.; Di, L. Drug-Like Properties: Concepts, Structure Design and Methods: from ADME to Toxicity
Optimization, Academic Press, Oxford, 2008.

Burger’s Medicinal Chemistry and Drug Discovery, 6th Edition, Vol. 1. Principles and Practice, edited by M. E.
Wolff, John Wiley & Sons: New York, 2003.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE454 DEEP LEARNING IN GENOMICS AND BIOMEDICINE L-T-P-C: 2-0-3-3

Course Objectives
 The goal of this course is to cover the overview of the relevant background in genomics.
 The course focuses the ongoing developments in deep learning applications of biomedical data.
 The course visualises the landscape of the genome.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Course Outcome

After completing this course, the students will be able to

CO1: Analyse the computational formats for representing genome.

CO2: Evaluate the open-source tools available for genome assembly.
CO3: Application of deep learning models available for genome annotations.
CO4: Create automated health database.

CO-PO Mapping

PO/PSO PSO3
CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
CO1 3 3 3 2 3 3 2 3 3 3 3
CO2 3 3 3 2 3 3 2 3 3 3 3 3 2
CO3 3 3 3 2 3 3 2 3 3 3 2 3 2
CO4 3 3 3 2 3 3 2 3 3 3 2 3
Syllabus

Unit 1

Introduction to deep learning - Applications of deep learning, Application of Deep learning to regulatory
genomics-metagenomics-variant scoring and population genetics - probability and statistics.

Unit 2

Applications of deep learning to predicting protein structure and pharmacogenomics - Applications of deep
learning to electronic health records and medical imaging data.

Textbooks / References

Polina Mamoshina, Armando Vieira, Evgeny Putin, Alex Zhavoronkov, Applications of deep learning in
Biomedicine, Mol.Pharmaceutics, 2016.

Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, Joel T Dudley, Deep learning for healthcare: review,
opportunities and challenges, Briefings in Bioinformatics, Vol.19, Issue.6, 2018.

Tianwei Yue, Haohan Wang, Deep Learning for Genomics: A Concise Overview, Handbook of Deep Learning
Applications, Springer, 2018.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE455 DNA SEQUENCING TECHNOLOGIES L-T-P-C: 2-0-3-3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Course Objectives

 The goal of this course is to cover the overview of DNA Sequencing.

 The course focus on the advancements in nucleic acid sequencing.

 The course emphasizes the interpretable, biological insights obtained from DNA Sequencing.

Course Outcomes

After completing this course, the students will be able to

CO1: Analyse the computational formats for representing read type in the DNA Sequencing.
CO2: Evaluate the open-source tools available for read-interpretations in DNA Sequencing.
CO3: Analyse the recent algorithms for signal-sequence conversion.
CO4: Create automated pipelines for the data analysis of comparative genomics.

CO-PO Mapping

PO/PSO PSO3
CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
CO1 3 3 3 2 3 3 2 3 3 3 3
CO2 3 3 3 2 3 3 2 3 3 3 3 2
CO3 3 3 3 2 3 3 2 3 3 3 3 2
CO4 3 3 3 2 3 3 2 3 3 3 2 3

Syllabus

Unit 1

Introduction to Genome Sequencing – Applying Euler’s theorem to assemble genomes - sequencing antibiotics -
Introduction to Structural Variation - Advantages of long-read sequencing for structural variation analysis -
Application of long-reads to structural variation analysis.

Unit 2

Data Analysis Tools for DNA sequencing - Accurate analysis of targeted genomic regions - Quantifying gene
expression and transcriptome analysis - Simultaneous analysis of epigenetic modifications and sequence data –
Metagenomic analysis of environmental samples - Applications of nanopore sequencing technologies to whole
genome sequencing of human viruses.

Textbooks/References

Sudmant, P.H. et al, An integrated map of structural variation in 2,504 human genomes. Nature. 2015.

Lu, H., Giordano, F. and Ning, Z, Oxford Nanopore MinION Sequencing and Genome Assembly. Genomics
Proteomics Bioinformatics, Vol.15, Issue.5, 2016.

Stankiewicz, P. and Lupski, J.R, Structural variation in the human genome and its role in disease. Annu Rev Med.
Vol. 61, 2010.

Evaluation Pattern

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE456 CRISPR TECHNOLOGY L-T-P-C: 2-0-3-3

Course Objectives

 The goal of this course is to cover the overview of the relevant background in crispr technology
and high-throughput biotechnology, focusing on the available data and their relevance.

 It will then cover the ongoing developments with the focus on the applications of these methods
to biomedical data.

Course Outcomes

After completing this course, the students will be able to

CO1: Analyse and learn the discovery of Crisper with emphasis to molecular mechanisms.
CO2: Understand a base knowledge on various application of gene therapy.
CO3: To become familiar with experimental design.
CO4: create automated pipelines for identifying the associations between multiple genome editions.

CO-PO Mapping

PO/PSO PSO3
CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2

CO1 3 3 3 2 3 3 3 3 3 3 3
CO2 3 3 3 2 3 3 3 3 3 3 3 2
CO3 3 3 3 2 3 3 3 3 3 3 3 2
CO4 3 3 3 2 3 3 3 3 3 3 2 3

Syllabus

Introduction to Genetic Engineering - History of Crispr – Crispr in bacteria – Classification of Crispr – General
structure of cas9 protein – Mechanism of Crispr cas9 – Applications – Database of Crispr – Case studies.

Textbooks/References

Maximilian Haeussler, Jean-Paul Concordet, CRISPOR Manual, MIT, 2016. Singh et al: A Mouse Geneticist’s
Practical Guide to CRISPR Applications; Genetics, Vol.199, No.1, 2015.

Ran et al, Genome engineering using the CRISPR-Cas9 system, Nature Protocols, 2013.

Fujihara&Ikawaw, CRISPR/Cas9-Based Genome Editing in Mice by Single Plasmid Injection, Methods Enzymol.
2014.

Evaluation Pattern:

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE457 FULL STACK DEVELOPMENT L-T-P-C: 2- 0- 3- 3

Course Objectives

 Full Stack Development is an indispensable course for computer science students. The course is
concerned with end-to-end development of a three-tier web application.
 It deals with the frameworks necessary to implement front-end, back-end and database covering design,
development and deployment.
 The course is designed to progress on both front-end and back-end in a synchronized fashion and
leverages GitHub and Heroku for version control and deployment.
 The course includes a term project to reinforce the technologies learnt.
Course Outcomes

After completing this course, students will be able to

CO1: Use markup and scripting languages to design and validate dynamic web pages.

CO2: Customize pages for users need based on responsive web design concepts.

CO3: Learn to design appropriate database services based on the requirements.

CO4: Design, develop and deploy an end-to-end web application as a term project.

CO-PO Mapping

PO/ PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO PSO

PSO 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3

CO

CO1 2 1 1 - 3 1 - - 2 - 2 1 2 1 1

CO2 2 1 1 2 3 1 - - 2 - 2 1 2 1 1

CO3 3 2 3 2 2 1 - - 2 - 2 2 2 2 2

CO4 2 2 2 2 2 1 - - 3 3 3 2 3 3 3

Syllabus
Introduction to web development, Git and GitHub, Taxonomy of frameworks. HTML basics – structuring,
positioning, alignment, CSS and JS basics, Browser development tools, Bootstrap basics. Basic Backend App
serving text/HTML and HTML from templates. Jinja template, Semantic tags, HTTP components – parameters,
headers, cookies, sessions, Handling forms, Serve-Handle JSON/XML requests, Intro to jQuery, jQuery request
handling and Ajax, more jinja templating, Lists and tables, DOM styling, Responsive design. Database creation

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 and connection, Creation of DB Schema from model, Adding relation between models, Intro to REST APIs, Basic
CRUD app, Form and tables for CRUD services. Authentication, designing error pages, setup default error pages.
Simple hosting on a public web host.

Text Books / References

Laura Lemay, Rafe Colburn, Jennifer Kyrnin, “Mastering HTML, CSS & JavaScript Web Publishing”,
Paperback, 2016.
Jon Duckett, “Web Design with HTML, CSS, JavaScript and jQuery”, Paperback, 2014.

Miguel Grinberg, “The New and Improved Flask Mega-Tutorial”, Paperback., 2017.

Kunal Relan, “Building REST APIs with Flask: Create Python Web Services with MySQL”, Paperback, 2019.

Evaluation Pattern:

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE458 MOBILE APPLICATION DEVELOPMENT L-T-P-C: 2-0-3-3

Course Objectives
 This is a hands-on elective course which introduces the fundamentals of native android application
development using Android Studio.
 The students will learn to customize activities and intents, create rich user interface and manage data on
databases such as SQLite.
 The course provides exposure to use various components such as services, async tasks, broadcast
receivers and content providers.
 The students also learn to use various APIs such as Maps, Sensors and GPS enabling them to develop
ready to use android applications for real-world use cases.

Course Outcomes
After completing this course, students will be able to
CO1: Understand the fundamental concepts of android operating system and android application development.
CO2: Understand the various building blocks of native android applications.
CO3: Design android specific user interface (UI).
CO4: Design and develop applications using android services and sensors.
CO5: Understand and apply data storage and sharing techniques for applications.

CO-PO Mapping

PO/ PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO PSO

PSO 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3

CO

CO1 2 2 2 3 3

CO2 3 2 2 2 3 3 2 3 3

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 CO3 2 1 3 3 3 3 3 3 2 3 3

CO4 2 2 2 3 3 3 3 3 3 3 3 3

CO5 3 2 3 3 3 3 3 3 3 2 3 3

Syllabus

Unit 1: Introduction and User Interface

Basics of Android - Android OS architecture, Versions, SDK, API Levels. Set up of mobile app development
environment - Understand the app idea and design user interface/wireframes of mobile application - Developing
and debugging mobile app components - First application - understanding file structure - layout and resource files
- deployment - emulators and devices.
Basic UI design - Button, EditText, TextView, basic event handlers. Activity - Lifecycle, Layouts - Selection
components - Radio, checkbox, Date/Time Picker. ListView, Grid view, ScrollView, Image view, Image buttons,
Spinner, Toggle, AutocompleteTextView.
Advanced UI design - Intents - Internal/External/Pending, Intent Filters, Android Manifest - Permissions -
Fragment, Fragment Lifecycle, Fragment communication - Menu, Notifications, Material Design, Navigation
Drawer, WebView.

Unit 2: Components
Data storage - SQLite, Shared Preferences, Internal/External Storage, Room Persistence Library. Background
Processing - Services - Started, Bound, Foreground, Intent Service - AsyncTasks. Broadcast receivers, Content
Providers, Content resolvers.

Unit 3: Sensors and Location API

Sensors - Motion sensors, Environmental, Position sensors. Touch sensors and Gesture detector. Location Based
Services - GPS and Google Maps. Apps with Connectivity to External APIs.

Text Book(s)
Burd B. Android application development all-in-one for dummies. John Wiley & Sons; 2015.

Reference(s)
AndroidDeveloperFundamentalsVersion2, 2018.Accessibleonline:
https://developer.android.com/courses/fundamentals-training/overview-v2
Darcey L, Conder S. Sams Teach Yourself Android Application Development in 24 Hours: Sams Teac Your
Andr Appl D_2. Pearson Education; 2011.
Hardy B, Phillips B. Android Programming: The Big Nerd Ranch Guide. Addison-Wesley Professional; 2013.

Evaluation Pattern:

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

22AIE459 USER EXPERIENCE DESIGN L-T-P-C 2-0-3- 3

Course Objectives

 This course provides a comprehensive overview of the user experience design process, and is intended
to familiarize students with the methods, concepts, and techniques necessary to make user experience
design an integral part of developing information interfaces.
 The course provides students with an opportunity to acquire the resources, skills, and hands-on
experience they need to design, develop, and evaluate information interfaces from a user-centered design
perspective.
 The students of this course will be able to apply the knowledge / learning’s from this course to their own
professional work as a user experience designer, UX Designers, Information Architects, Usability
Engineers etc. in IT domain. They will able to apply learning’s in designing the Website design, Mobile
applications, Enterprise and consumer software products and applications.

Course Outcomes
After completing this course, students will be able to

CO1: Define the critical issues and theoretical underpinnings of User Experience (UX) design.

CO2: Establish requirements for UX design concepts.

CO3: Develop alternatives for UX design concepts and demonstrate the construction of UX design artifacts.

CO4: Evaluate Ux Design artifacts.

CO5: Learn how Ux design concepts are applied for real life problems.

CO-PO Mapping

PO/ PO PO PO PO PO PO PO PO PO PO PO PO PSO PSO PSO

PSO 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3

CO

CO1 1 2 2 3 3

CO2 3 1 1 1 3 2

CO3 3 1 3 2 1 3 2

CO4 3 3 3 3 3 2 2 1 2 3 3

CO5 3 3 3 3 2 2 3 2 3 2 2 3 3

Syllabus

Unit 1

Ux Introduction: User Interaction with the products, applications and services – Cognitive Model/Mental Model,
Principles of Ux Design, Elements of Ux design - Core elements of User Experience. How these elements work
together; Ux Design Process - Defining the UX Design Process and Methodology, Research and Define –

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Importance of research, Research methods and tools, Understanding the User Needs and Goals, Understanding
the Business Goals, Deliverables of the Research & Define phase, Insight on User Goals and Business Goals.

Unit 2
Ux Design Process Ideate and Design - Visual Design Principles, Information Design and Data Visualizatiion,
Interaction Design, Information Architecture, Wireframing & Storyboarding, UI Elements and Widgets, Screen
Design and Layouts, Prototype and Test – Need for design testing, Definition of Usability Testing, Types of
Usability Testing, Usability Testing Process, Prepare and plan for the Usability Tests, Prototype Design to Test,
Introduction of prototyping tools, Conducting Usability Tests, Communicating Usability Test Results.

Unit 3

Ux Design Process Iterate and Improve - Understanding the Usability Test findings, Applying the Usability Test
feedback in improving the design, Deliver - Communication with implementation team, UX Deliverables to be
given to implementation team, Ux Metrics – Overview, Types of metrics – CSAT, NPS, SUS, TPI, Choosing the
right metrics, Future of Ux Design, Case studies: Commuter Rail Mobile App, Medical Patient portal, Ux Tools
– Wireframing Ux Design tools such as Pencil, MockPlus, UxPin Usability Testing Tools – Optimizely,
ClickHeat, Chalkmark

Text Book(s)

1. Platt D. The Joy of UX: User Experience and interactive design for developers. Addison-Wesley
Professional; 2016.

Reference(s)

1. Garrett JJ. The elements of user experience: user-centered design for the Web and beyond (2. painos).
Berkeley: New Riders; 2011.
2. Goodman E, Kuniavsky M, Moed A. Observing the user experience: A practitioner's guide to user
research. Elsevier; 2012.
3. Buxton B. Sketching User Experiences: Getting the Design Right and the Right Design. Morgan
Kaufmann; 2010.
4. Shneiderman B, Plaisant C. Designing the User Interface: Strategies for Effective Human-Computer
Interaction. Pearson Education India; 2010.
5. Tenner E. The Design of Everyday Things by Donald Norman. Technology and Culture; 2015.

Evaluation Pattern:

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

22AIE460 SOFTWARE DESIGN PATTERNS L-T-P-C 2-0-3- 3

Course Description and Objectives: Design patterns are a general repeatable solution to a commonly occurring
software design problem and represent the best practices of experienced object-oriented software designers and
developers. Design patterns accelerates the development process by providing time tested solutions that enhance
the readability and maintainability of code across a broad spectrum of software developers, designers and
architects familiar with patterns. This course provides an overview of the important design patterns and focuses
on their applicability to various design problems. This course helps a student with basic knowledge of object-
oriented design and programming become a more efficient and effective software professional.

Course Outcomes
After completing this course, students will be able to

CO1: Understand the common software design problems seen in the development process

CO2: Demonstrate the use of various design patterns to tackle these common problems.

CO3: Identify the most suitable design pattern to address a given software design problem.

CO4: Analyze existing code for anti-patterns and refactor the code.

CO5: Apply best practices of design principles for software design and development.

CO-PO Mapping

PO/PS P PO PO PO PO PO PO PO PO PO PO PO PSO PSO PSO

O O 2 3 4 5 6 7 8 9 10 11 12 1 2 3
1
CO

CO1 2 3 2 2 3 2 2 2 1 2 2 3 3 3

CO2 3 3 3 3 3 3 2 2 3 2 3 3 3 3

CO3 3 3 3 3 3 3 2 2 3 2 3 3 3 3

CO4 3 3 3 3 3 2 2 3 2 1 2 3 3 3

CO5 3 3 3 3 3 2 2 3 2 1 3 3 3 3

Syllabus

Unit 1
Introduction to Design Patterns: Significance – Software Design and patterns – Model – View - Controller.

Unit 2

Observer Pattern - Decorator Pattern - Factory Pattern - Singleton Pattern - Command Pattern - Adapter and
Facade Patterns - Template

Method Pattern - Iterator and Composite Patterns – The State Pattern – The Proxy Pattern – Compound Patterns.

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Unit 3
GRASP Patterns and Anti-patterns. Case Study: Use of patterns in the Design of a Modern Web Framework.

TEXTBOOK/ REFERENCES:
Erich Freeman, Elisabeth Robson, Bert Bates and Kathy Sierra “Head First Design Patterns”, O’Reilly Media
Inc., October 2004.

Erich Gamma, Richard Helm, Ralph Johnson and John M. Vlissides, “Design Patterns: Elements of Reusable
Object-Oriented Software”, Second Edition, Addison Wesley, 2000

James W. Cooper, “Java Design Patterns: A Tutorial”, Second Edition, Pearson Education, 2003.

Mark Grand, “Patterns in Java – A Catalog of Reusable Patterns Illustrated with UML”, Wiley – Dream tech
India, 2002

Evaluation Pattern:

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE461 CONCURRENT PROGRAMMING L-T-P-C: 2-0-3-3

Course overview:
The course aims to provide fundamentals of concurrency and expose students to the various concurrent
frameworks that includes multi-threaded and parallel frameworks. Although, the content of the course is centred
around Java, the underlying concepts are general and applicable irrespective of the languages. The course will
provide hands-on exposure to various subtleties in concurrent programming which are key for software
developers.

Course Outcome
After completing this course, the students will be able to

CO1: Understand and appreciate the associated with concurrent programming.

CO2: Get a hands-on exposure to a multi-threaded programming framework in Java.

CO3: Get a hands-on exposure to a parallel programming framework in Java.

CO4: Understand the use of concurrent data structures and synchronization utilities

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

CO-PO Mapping

PO/ PS
PS PO1 PO P P PO PO PO PO PO PO PO PO PS PS O3
O 2 O3 O4 5 6 7 8 9 10 11 12 O1 O2
CO
CO 1 1 2 2 3
1
CO 1 1 3 2 3
2
CO 1 2 2 2 3
3
CO 1 2 2 2 3
4

Syllabus

Unit 1

Basic concurrency concepts, problems with concurrent applications – data races, deadlocks, live-locks, resource starvation,
priority inversion, Designing concurrent applications – analysis-design-implementation-testing-tuning, Java concurrency API
– Threads in Java.

Unit 2

Managing lots of threads – basic components of executor framework, serial vs. coarse grained vs. fine grained concurrency
with examples, Concurrency in a client/server environment, Callable and Future interfaces, running tasks divided into phases
using Phaser class.

Unit 3

Fork-Join parallel programming framework – Divide-and-conquer, Recursive Action Task, ForkJoinPool, and
ExecutorService, Work stealing. Processing massive dataset with Parallel Streams – Concurrent Loader, Concurrent Statistics,
Concurrent data structures and synchronization utilities.

Textbooks

Javier Fernández González, Mastering Concurrency with Java 9, Second Edition, Pakt Publishing, July 2017.

Brian Goetz, Java Concurrency in Practice. Addison Wesley, 2010.

Herbert Schidlt, Java Complete Reference, Eleventh Edition, Paperback, 2020.

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

 22AIE462 DEEP REINFORCEMENT LEARNING L-T-P-C: 2- 0- 3- 3

Course Objectives
 This course aims to provide the cutting-edge concepts in deep reinforcement learning
 It also helps the students to train an agent which can perform a variety of complex tasks.
 It will also help students to learn about the core challenges and approaches, including generalization and
exploration and also make the students well versed in the key ideas and techniques for deep reinforcement
learning

Course Outcomes

After completing this course, the students will be able to

CO1: Decide whether a given application problem should be formulated as a Deep Reinforcement Learning
(DRL) problem.

CO2: Correctly define the problem formulation, design the most suitable algorithm from the different possible
classes of DRL algorithms, providing a justification

CO3: Implement and apply temporal-difference reinforcement learning algorithms

CO4: Apply the multiple criteria for analysing and evaluating the DRL algorithms on the relevant metrics:
regret, sample complexity, computational complexity, empirical performance, convergence.

CO5: Implement in code the main DRL algorithms and apply it to solve several practical problems in different
application domains, evaluating experimentally their performance

CO-PO Mapping

PO PO PO PO PO PO PO PO PO PO1 PO1 PO1 PSO PSO PSO

1 2 3 4 5 6 7 8 9 0 1 2 1 2 3

CO 3 3 3 3 3 - - - 3 2 2 3 3 3 3
1

CO 3 3 3 3 3 - - - 3 2 2 3 3 3 3
2

CO 3 3 3 3 3 - - - 3 2 2 3 3 3 3
3

CO 3 3 3 3 3 - - - 3 2 2 3 3 3 3
4

CO 3 3 3 3 3 - - - 3 2 2 3 3 3 3
5

Syllabus
Introduction to Deep Reinforcement Learning – Approximate Solution Methods: On-policy Prediction with
Approximation – On-policy Control with Approximation – Off-policy Methods with Approximation –
Eligibility Traces – Policy Gradient Methods – Applications and Case studies.

Text Books / Reference Books

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

‘Reinforcement Learning’, Richard.S.Sutton and Andrew G.Barto, Second edition, MIT Press, 2018

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

22AIE463 TIME SERIES ANALYSIS L-T-P-C 2-0-3- 3

Course Objectives

 This course will cover the tools and techniques required to analyse time series data

 The course will focus on the linear time series analysis, nonlinear time series analysis and ML/DL
methods for predictive analytics.

 The course will also focus on generating models from non-stationary and stationary time series data.

Course Outcomes

After completing this course, students will be able to

CO1: Analyse linear time series data

CO2: Analyse nonlinear time series data

CO3: Analyse stationary and non-stationary time series data

CO4: Apply ML/DL models to perform predictive analytics on time series data

CO-PO Mapping

CO/PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO3

CO1 2 3 2 2 3 - - - 1 2 - 2 2 1 -

CO2 2 3 2 2 3 - - - 1 2 - 2 2 1 -

CO3 2 3 3 2 3 - - - 1 2 - 2 2 1 -

CO4 2 3 3 2 3 - - - 1 2 - 2 2 1 3

Syllabus

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022

Unit 1
Introduction – Review of basic statistics – Stationarity – Ergodicity – Autocorrelation – Partial Autocorrelation –
Linear Models – Autoregressive Models – Moving Average Models

Unit 2
ARMA – ARIMA – SARIMA – VAR – Conditional Heteroscedastic Models – ARCH Model – GARCH Model

Unit 3

Nonlinear Models – Tests for Stationarity – Tests for nonlinearity – State Space Models

Unit 4
Machine Learning Models – Deep Learning Models –Precursors for Catastrophic Transitions.
Text Books / References

Jonathan D Cryer & Kung Silk Chan, Time Series Analysis With Applications in R, Second Edition, Springer,
2008

Robert H Shumway & David S Stoffer, Time Series Analysis and Its Applications with R examples, Third
Edition, Springer,2011

G E P Box, G M Jenkins, G C Reinsel, G M Ljung, Time Series Analysis: Forecasting and Control, fifth edition,
Wiley, 2016

Aileen Nielsen, Practical Time Series Analysis Prediction with Statistics and Machine Learning, O’Reilly, first
edition, 2019

Evaluation Pattern

Assessment Internal/External Weightage (%)

Assignments (Minimum 2) Internal 30

Quizzes (Minimum 2) Internal 20

Mid-Term Examination Internal 20

Term Project/ End Semester Examination External 30

Amrita Vishwa Vidyapeetham. BTC-AIE B.Tech Curriculum June 2022