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Artificial Intelligence and Machine Learning: Visvesvaraya Technological University, Belagavi

The document outlines the scheme of teaching and examinations for the third semester of the BE program in Artificial Intelligence and Machine Learning. It lists 10 courses that will be taught in the semester along with details about teaching hours, examination structure, and credits for each course.

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0% found this document useful (0 votes)

190 views50 pages

Artificial Intelligence and Machine Learning: Visvesvaraya Technological University, Belagavi

Uploaded by

Addy Rao

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1

VISVESVARAYA TECHNOLOGICAL UNIVERSITY, BELAGAVI

3rd and 4th Semester BE
Artificial Intelligence and Machine Learning

Scheme of Teaching and Examinations

Outcome Based Education (OBE) and Choice Based Credit System (CBCS)
(Effective from the academic year 2018 – 19)

Artificial Intelligence and Machine Learning

VISVESVARAYA TECHNOLOGICAL UNIVERSITY, BELAGAVI

Scheme of Teaching and Examination 2018 – 19
Artificial Intelligence and Machine Learning
Outcome Based Education (OBE) and Choice Based Credit System (CBCS)
(Effective from the academic year 2018 – 19)
III SEMESTER
Teaching Hours /Week Examination

Department
Teaching

Practical/

Credits
Drawing

Total Marks
Tutorial

CIE Marks
Lecture

SEE Marks
Duration in
Theory
Sl.
Course and Course Title

hours
No
Course Code

L T P
---
BSC 18MAT31 Transform Calculus, Fourier Series Mathematics 2 2 03 40 60 100 3
1
And Numerical Techniques
2 PCC 18CS32 Data Structures and Applications CS / IS 3 2 -- 03 40 60 100 4
3 PCC Analog and Digital Electronics CS / IS -- 40 60 100 3
18CS33 3 0 03
4 PCC 18CS34 Computer Organization CS / IS 3 0 -- 03 40 60 100 3
5 PCC Software Engineering CS / IS -- 40 60 100 3
18CS35 3 0 03
6 PCC 18CS36 Discrete Mathematical Structures CS / IS 3 0 -- 03 40 60 100 3
7 PCC Analog and Digital Electronics CS / IS -- 2 2 03 40 60 100 2
18CSL37
Laboratory
8 PCC 18CSL38 Data Structures Laboratory CS / IS -- 2 2 03 40 60 100 2

18KVK39 Vyavaharika Kannada (Kannada

for communication)/ -- 2 -- -- 100 --
18KAK39 Aadalitha Kannada (Kannada for
Administration)
9 HSMC HSMC 100 1
OR OR

1 -- -- 02 40 60
18CPH39 Constitution of India, Professional
Ethics and Cyber Law Examination is by objective type questions

17 10 24 420 480

TOTAL OR OR 04 OR OR OR 900 24

18 08 27 360 540

Note: BSC: Basic Science, PCC: Professional Core, HSMC: Humanity and Social Science, NCMC: Non-credit mandatory course
18KVK39 Vyavaharika Kannada (Kannada for communication) is for non-Kannada speaking, reading and writing students and 18KAK39 Aadalitha
Kannada (Kannada for Administration) is for students who speak, read and write Kannada.
Course prescribed to lateral entry Diploma holders admitted to III semester of Engineering programs
10 NCMC 18MATDIP31 Additional Mathematics - I Mathematics 02 01 -- 03 40 60 100 0
(a)The mandatory non – credit courses Additional Mathematics I and II prescribed for III and IV semesters respectively, to the lateral entry Diploma
holders admitted to III semester of BE/B.Tech programs, shall attend the classes during the respective semesters to complete all the formalities of the
course and appear for the University examination. In case, any student fails to register for the said course/ fails to secure the minimum 40 % of the
prescribed CIE marks, he/she shall be deemed to have secured F grade. In such a case, the student have to fulfil the requirements during subsequent
semester/s to appear for SEE.
(b) These Courses shall not be considered for vertical progression, but completion of the courses shall be mandatory for the award of degree
Courses prescribed to lateral entry B. Sc degree holders admitted to III semester of Engineering programs
Lateral entrant students from B.Sc. Stream, shall clear the non-credit courses Engineering Graphics and Elements of Civil Engineering and Mechanics
of the First Year Engineering Programme. These Courses shall not be considered for vertical progression, but completion of the courses shall be

Artificial Intelligence and Machine Learning

3
mandatory for the award of degree.

AICTE Activity Points to be earned by students admitted to BE/B.Tech/B. Plan day college programme (For more details refer to Chapter
6,AICTE Activity Point Programme, Model Internship Guidelines): Over and above the academic grades, every Day College regular student
admitted to the 4 years Degree programme and every student entering 4 years Degree programme through lateral entry, shall earn 100 and 75 Activity
Points respectively for the award of degree through AICTE Activity Point Programme. Students transferred from other Universities to fifth semester are
required to earn 50 Activity Points from the year of entry to VTU. The Activity Points earned shall be reflected on the student’s eighth semester Grade
Card. The activities can be can be spread over the years, anytime during the semester weekends and holidays, as per the liking and convenience of the
student from the year of entry to the programme. However, minimum hours’ requirement should be fulfilled. Activity Points (non-credit) have no effect
on SGPA/CGPA and shall not be considered for vertical progression. In case students fail to earn the prescribed activity Points, Eighth semester Grade
Card shall be issued only after earning the required activity Points. Students shall be admitted for the award of degree only after the release of the
Eighth semester Grade Card.

Artificial Intelligence and Machine Learning

VISVESVARAYA TECHNOLOGICAL UNIVERSITY, BELAGAVI

Department
Course and

Practical/
Teaching

Drawing
Tutorial
Sl.

Lecture

Credits
Theory

Total Marks
CIE Marks

SEE Marks
Duration in
Course Title
No

hours
Course Code

L T P

Complex Analysis, Probability

BSC 18MAT41 Mathematics 2 2 03 40 60 100 3
1 And Statistical Methods --
2 PCC 18CS42 Design and Analysis of CS / IS 3 2 -- 03 40 60 100 4
Algorithms
3 PCC 18CS43 Operating Systems CS / IS 3 0 -- 03 40 60 100 3
4 PCC Microcontroller and Embedded CS / IS -- 40 60 100 3
18SC44 3 0 03
Systems
5 PCC Object Oriented Concepts CS / IS 3 0 -- 03 40 60 100 3
18CS45
6 PCC 18CS46 Data Communication CS / IS 3 0 -- 03 40 60 100 3
7 PCC 18CSL47 Design and Analysis of Algorithm CS / IS -- 2 2 03 40 60 100 2
Laboratory
8 PCC Microcontroller and Embedded CS / IS -- 2 2 40 60 100 2
18CSL48 03
Systems Laboratory
18KVK49 Vyavaharika Kannada (Kannada
for communication)/ -- 2 -- -- 100 --
18KAK49 Aadalitha Kannada (Kannada for
Administration)
HSMC HSMC 100 1
9 OR OR
1 -- -- 02 40 60
Constitution of India, Professional
18CPH49
Ethics and Cyber Law
Examination is by objective type questions

17 10 24 420 480
TOTAL OR OR OR OR OR
04 900 24
18 08 27 360 540

Note: BSC: Basic Science, PCC: Professional Core, HSMC: Humanity and Social Science, NCMC: Non-credit mandatory course

18KVK49 Vyavaharika Kannada (Kannada for communication) is for non-Kannada speaking, reading and writing students and 18KAK49 Aadalitha
Kannada (Kannada for Administration) is for students who speak, read and write Kannada.
Course prescribed to lateral entry Diploma holders admitted to III semester of Engineering programs

10 NCMC 18MATDIP41 Additional Mathematics - II Mathematics 02 01 -- 03 40 60 100 0

(a)The mandatory non – credit courses Additional Mathematics I and II prescribed for III and IV semesters respectively, to the lateral entry Diploma
holders admitted to III semester of BE/B.Tech programs, shall attend the classes during the respective semesters to complete all the formalities of the
course and appear for the University examination. In case, any student fails to register for the said course/ fails to secure the minimum 40 % of the
prescribed CIE marks, he/she shall be deemed to have secured F grade. In such a case, the student have to fulfil the requirements during subsequent
semester/s to appear for SEE.

(b) These Courses shall not be considered for vertical progression, but completion of the courses shall be mandatory for the award of degree

Artificial Intelligence and Machine Learning

Courses prescribed to lateral entry B. Sc degree holders admitted to III semester of Engineering programs

Lateral entrant students from B.Sc. Stream, shall clear the non-credit courses Engineering Graphics and Elements of Civil Engineering and Mechanics
of the First Year Engineering Programme. These Courses shall not be considered for vertical progression, but completion of the courses shall be
mandatory for the award of degree.

AICTE activity Points: In case students fail to earn the prescribed activity Points, Eighth semester Grade Card shall be issued only after earning the
required activity Points. Students shall be admitted for the award of degree only after the release of the Eighth semester Grade Card.

Artificial Intelligence and Machine Learning

B. E. COMMON TO ALL PROGRAMMES

Choice Based Credit System (CBCS) and Outcome Based Education (OBE)
SEMESTER - III
TRANSFORM CALCULUS, FOURIER SERIES AND NUMERICAL TECHNIQUES
Course Code 18MAT31 CIE Marks 40
Teaching Hours/Week (L: T:P) (2:2:0) SEE Marks 60
Credits 03 Exam Hours 03
Course Learning Objectives:
• To have an insight into Fourier series, Fourier transforms, Laplace transforms, Difference
equations and Z-transforms.
• To develop the proficiency in variational calculus and solving ODE’s arising in engineering
applications, using numerical methods.
Module-1
Laplace Transform: Definition and Laplace transforms of elementary functions (statements only).
Laplace transforms of Periodic functions (statement only) and unit-step function – problems.
Inverse Laplace Transform: Definition and problems, Convolution theorem to find the inverse
Laplace transforms (without Proof) and problems. Solution of linear differential equations using
Laplace transforms.
Module-2
Fourier Series: Periodic functions, Dirichlet’s condition. Fourier series of periodic functions period
2π and arbitrary period. Half range Fourier series. Practical harmonic analysis.
Module-3
Fourier Transforms: Infinite Fourier transforms, Fourier sine and cosine transforms. Inverse Fourier
transforms. Problems.
Difference Equations and Z-Transforms: Difference equations, basic definition, z-transform-
definition, Standard z-transforms, Damping and shifting rules, initial value and final value theorems
(without proof) and problems, Inverse z-transform and applications to solve difference equations.
Module-4
Numerical Solutions of Ordinary Differential Equations(ODE’s):
Numerical solution of ODE’s of first order and first degree- Taylor’s series method, Modified Euler’s
method. Runge -Kutta method of fourth order, Milne’s and Adam-Bash forth predictor and corrector
method (No derivations of formulae)-Problems.
Module-5
Numerical Solution of Second Order ODE’s: Runge-Kutta method and Milne’s predictor and
corrector method. (No derivations of formulae).
Calculus of Variations: Variation of function and functional, variational problems, Euler’s equation,
Geodesics, hanging chain, problems.
Course outcomes: At the end of the course the student will be able to:
• CO1: Use Laplace transform and inverse Laplace transform in solving differential/ integral
equation arising in network analysis, control systems and other fields of engineering.
• CO2: Demonstrate Fourier series to study the behaviour of periodic functions and their
applications in system communications, digital signal processing and field theory.
• CO3: Make use of Fourier transform and Z-transform to illustrate discrete/continuous function
arising in wave and heat propagation, signals and systems.
• CO4: Solve first and second order ordinary differential equations arising in engineering
problems using single step and multistep numerical methods.
• CO5:Determine the externals of functionals using calculus of variations and solve
problems arising in dynamics of rigid bodies and vibrational analysis.
Question paper pattern:
• The question paper will have ten full questions carrying equal marks.
Artificial Intelligence and Machine Learning
7

• Each full question will be for 20 marks.

• There will be two full questions (with a maximum of four sub- questions) from each module.
• Each full question will have sub- question covering all the topics under a module.
• The students will have to answer five full questions, selecting one full question from each
module.
Sl. Name of the Name of the Edition and
Title of the Book
No. Author/s Publisher Year
Textbooks
1 Advanced Engineering E. Kreyszig John Wiley & Sons 10th Edition,
Mathematics 2016
2 Higher Engineering B. S. Grewal Khanna Publishers 44th Edition,
Mathematics 2017
3 Engineering Mathematics Srimanta Pal et Oxford University 3rd Edition,
al Press 2016
Reference Books
1 Advanced Engineering C. Ray Wylie, McGraw-Hill Book 6th Edition,
Mathematics Louis C. Barrett Co 1995
2 Introductory Methods of S.S.Sastry Prentice Hall of India 4th Edition 2010
Numerical Analysis
3 Higher Engineering B.V. Ramana McGraw-Hill 11th
Mathematics Edition,2010
4 A Textbook of Engineering N.P.Bali and Laxmi Publications 6th Edition,
Mathematics Manish Goyal 2014
5 Advanced Engineering Chandrika Khanna Publishing, 2018
Mathematics Prasad and
Reena Garg
Web links and Video Lectures:
1. http://nptel.ac.in/courses.php?disciplineID=111
2. http://www.class-central.com/subject/math(MOOCs)
3. http://academicearth.org/
4. VTU EDUSAT PROGRAMME - 20

Artificial Intelligence and Machine Learning

B. E. COMMON TO ALL PROGRAMMES

• Each full question will be for 20 marks.

Artificial Intelligence and Machine Learning

DATA STRUCTURES AND APPLICATIONS

(Effective from the academic year 2018 -2019)
SEMESTER – III
Course Code 18CS32 CIE Marks 40
Number of Contact Hours/Week 3:2:0 SEE Marks 60
Total Number of Contact Hours 50 Exam Hours 03
CREDITS –4
Course Learning Objectives: This course (18CS32) will enable students to:
• Explain fundamentals of data structures and their applications essential for
programming/problem solving.
• Illustrate linear representation of data structures: Stack, Queues, Lists, Trees and
Graphs.
• Demonstrate sorting and searching algorithms.
• Find suitable data structure during application development/Problem Solving.
Module 1 Contact
Hours
Introduction: Data Structures, Classifications (Primitive & Non 10
Primitive), Data structure Operations, Review of Arrays, Structures, Self-
Referential Structures, and Unions. Pointers and Dynamic Memory
Allocation Functions. Representation of Linear Arrays in Memory,
Dynamically allocated arrays.

Array Operations: Traversing, inserting, deleting, searching, and sorting.

Multidimensional Arrays, Polynomials and Sparse Matrices.

Strings: Basic Terminology, Storing, Operations and Pattern Matching

algorithms. Programming Examples.

Textbook 1: Chapter 1: 1.2, Chapter 2: 2.2 - 2.7 Text Textbook 2: Chapter 1:

1.1 - 1.4,
Chapter 3: 3.1 - 3.3, 3.5, 3.7, Ch apter 4: 4.1 - 4.9, 4.14 Reference 3: Chapter
1: 1.4
RBT: L1, L2, L3
Module 2
Stacks: Definition, Stack Operations, Array Representation of Stacks, 10
Stacks using Dynamic Arrays, Stack Applications: Polish notation, Infix
to postfix conversion, evaluation of postfix expression.

Recursion - Factorial, GCD, Fibonacci Sequence, Tower of Hanoi,

Ackerman's function. Queues: Definition, Array Representation, Queue
Operations, Circular Queues, Circular queues using Dynamic arrays,
Dequeues, Priority Queues, A Mazing Problem. Multiple Stacks and
Queues. Programming Examples.

Textbook 1: Chapter 3: 3.1 -3.7 Textbook 2: Chapter 6: 6.1 -6.3, 6.5, 6.7-6.10,
6.12, 6.13
RBT: L1, L2, L3

Module 3
Linked Lists: Definition, Representation of linked lists in Memory, 10
Memory allocation; Garbage Collection. Linked list operations:

Artificial Intelligence and Machine Learning

Traversing, Searching, Insertion, and Deletion. Doubly Linked lists,

Circular linked lists, and header linked lists. Linked Stacks and Queues.
Applications of Linked lists – Polynomials, Sparse matrix representation.
Programming Examples

Textbook 1: Ch apter 4: 4.1 – 4.6, 4.8, Textbook 2: Ch apter 5: 5.1 – 5.10,

RBT: L1, L2, L3
Module 4
Trees: Terminology, Binary Trees, Properties of Binary trees, Array and linked 10
Representation of Binary Trees, Binary Tree Traversals - Inorder, postorder,
preorder; Additional Binary tree operations. Threaded binary trees, Binary Search
Trees – Definition, Insertion, Deletion, Traversal, Searching, Application of
Trees-Evaluation of Expression, Programming Examples
Textbook 1: Chapter 5: 5.1 –5.5, 5.7; Textbook 2: Chapter 7: 7.1 – 7.9
RBT: L1, L2, L3
Module 5
Graphs: Definitions, Terminologies, Matrix and Adjacency List Representation 10
Of Graphs, Elementary Graph operations, Traversal methods: Breadth First
Search and Depth First Search.
Sorting and Searching: Insertion Sort, Radix sort, Address Calculation Sort.
Hashing: Hash Table organizations, Hashing Functions, Static and Dynamic
Hashing.
Files and Their Organization: Data Hierarchy, File Attributes, Text Files and
Binary Files, Basic File Operations, File Organizations and Indexing
Textbook 1: Chapter 6 : 6.1 –6.2, Chapter 7:7.2, Chapter 8 : 8.1-8.3
Textbook 2: Chapter 8 : 8.1 – 8.7, Chapter 9 : 9.1-9.3, 9.7, 9.9
Reference 2: Chapter 16 : 16.1 - 16.7
RBT: L1, L2, L3
Course Outcomes: The student will be able to :
• Use different types of data structures, operations and algorithms
• Apply searching and sorting operations on files
• Use stack, Queue, Lists, Trees and Graphs in problem solving
• Implement all data structures in a high-level language for problem solving.
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Ellis Horowitz and Sartaj Sahni, Fundamentals of Data Structures in C, 2nd Ed,
Universities Press, 2014.
2. Seymour Lipschutz, Data Structures Schaum's Outlines, Revised 1st Ed, McGraw Hill,
2014.
Reference Books:
nd
1. Gilberg & Forouzan, Data Structures: A Pseudo-code approach with C, 2 Ed,
Cengage Learning,2014.
2. Reema Thareja, Data Structures using C, 3rd Ed, Oxford press, 2012.

Artificial Intelligence and Machine Learning

3. Jean-Paul Tremblay & Paul G. Sorenson, An Introduction to Data Structures with

Applications, 2nd Ed, McGraw Hill, 2013
4. A M Tenenbaum, Data Structures using C, PHI, 1989
5. Robert Kruse, Data Structures and Program Design in C, 2nd Ed, PHI, 1996.

Artificial Intelligence and Machine Learning

ANALOG AND DIGITAL ELECTRONICS

(Effective from the academic year 2018 -2019)
SEMESTER – III
Course Code 18CS33 CIE Marks 40
Number of Contact Hours/Week 3:0:0 SEE Marks 60
Total Number of Contact Hours 40 Exam Hours 03
CREDITS –3
Course Learning Objectives: This course (18CS33) will enable students to:
• Explain the use of photoelectronics devices, 555 timer IC, Regulator ICs and uA741
opamap IC
• Make use of simplifying techniques in the design of combinational circuits.
• Illustrate combinational and sequential digital circuits
• Demonstrate the use of flipflops and apply for registers
• Design and test counters, Analog-to-Digital and Digital-to-Analog conversion
techqniues.

Module 1 Contact
Hours
Photodiodes, Light Emitting Diodes and Optocouplers ,BJT Biasing :Fixed bias 08
,Collector to base Bias , voltage divider bias, Operational Amplifier Application
Circuits: Multivibrators using IC-555, Peak Detector, Schmitt trigger, Active
Filters, Non-Linear Amplifier, Relaxation Oscillator, Current-to-Voltage and
Voltage-to-Current Converter , Regulated Power Supply Parameters, adjustable
voltage regulator ,D to A and A to D converter.

Text Book 1 :Part A:Chapter 2(Section 2.9,2.10,2.11), Chapter 4(Section 4.2

,4.3,4.4),Chapter 7 (section (7.2,7.3.1,7.4,7.6 to 7.11), Chapter 8 (section
(8.1,8.5), Chapter 9

RBT: L1, L2
Module 2
Karnaugh maps: minimum forms of switching functions, two and three variable 08
Karnaugh maps, four variable karnaugh maps, determination of minimum
expressions using essential prime implicants, Quine-McClusky Method:
determination of prime implicants, The prime implicant chart, petricks method,
simplification of incompletely specified functions, simplification using map-
entered variables

Text book 1:Part B: Chapter 5 ( Sections 5.1 to 5.4) Chapter 6(Sections 6.1 to
6.5)

RBT: L1, L2
Module 3
Combinational circuit design and simulation using gates: Review of 08
Combinational circuit design, design of circuits with limited Gate Fan-in ,Gate
delays and Timing diagrams, Hazards in combinational Logic, simulation and
testing of logic circuits

Multiplexers, Decoders and Programmable Logic Devices: Multiplexers, three

Artificial Intelligence and Machine Learning

state buffers, decoders and encoders, Programmable Logic devices,

Programmable Logic Arrays, Programmable Array Logic.
Text book 1:Part B: Chapter 8,Chapter 9 (Sections 9.1 to 9.6)
RBT: L1, L2
Module 4
Introduction to VHDL: VHDL description of combinational circuits, VHDL 08
Models for multiplexers, VHDL Modules.

Latches and Flip-Flops: Set Reset Latch, Gated Latches, Edge-Triggered D Flip
Flop 3,SR Flip Flop, J K Flip Flop, T Flip Flop, Flip Flop with additional inputs,
Asynchronous Sequential Circuits
Text book 1:Part B: Chapter 10(Sections 10.1 to 10.3),Chapter 11 (Sections
11.1 to 11.9)
RBT: L1, L2
Module 5
Registers and Counters: Registers and Register Transfers, Parallel Adder with 08
accumulator, shift registers, design of Binary counters, counters for other
sequences, counter design using SR and J K Flip Flops, sequential parity checker,
state tables and graphs
Text book 1:Part B: Chapter 12(Sections 12.1 to 12.5),Chapter 13(Sections
13.1,13.3
RBT: L1, L2
Course Outcomes: The student will be able to :
• Design and analyze application of analog circuits using photo devices, timer IC,
power supply and regulator IC and op-amp.
• Explain the basic principles of A/D and D/A conversion circuits and develop the
same.
• Simplify digital circuits using Karnaugh Map , and Quine-McClusky Methods
• Explain Gates and flip flops and make us in designing different data processing
circuits, registers and counters and compare the types.
• Develop simple HDL programs
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Charles H Roth and Larry L Kinney, Analog and Digital Electronics, Cengage
Learning,2019
Reference Books:
1. Anil K Maini, Varsha Agarwal, Electronic Devices and Circuits, Wiley, 2012.
2. Donald P Leach, Albert Paul Malvino & Goutam Saha, Digital Principles and
Applications, 8th Edition, Tata McGraw Hill, 2015.
3. M. Morris Mani, Digital Design, 4th Edition, Pearson Prentice Hall, 2008.
4. David A. Bell, Electronic Devices and Circuits, 5th Edition, Oxford University Press,
2008

Artificial Intelligence and Machine Learning

COMPUTER ORGANIZATION
(Effective from the academic year 2018 -2019)
SEMESTER – III
Course Code 18CS34 CIE Marks 40
Number of Contact Hours/Week 3:0:0 SEE Marks 60
Total Number of Contact Hours 40 Exam Hours 03
CREDITS –3
Course Learning Objectives: This course (18CS34) will enable students to:
• Explain the basic sub systems of a computer, their organization, structure and
operation.
• Illustrate the concept of programs as sequences of machine instructions.
• Demonstrate different ways of communicating with I/O devices and standard I/O
interfaces.
• Describe memory hierarchy and concept of virtual memory.
• Describe arithmetic and logical operations with integer and floating-point operands.
• Illustrate organization of a simple processor, pipelined processor and other computing
systems.
Module 1 Contact
Hours
Basic Structure of Computers: Basic Operational Concepts, Bus Structures, 08
Performance – Processor Clock, Basic Performance Equation, Clock Rate,
Performance Measurement. Machine Instructions and Programs: Memory
Location and Addresses, Memory Operations, Instructions and Instruction
Sequencing, Addressing Modes, Assembly Language, Basic Input and Output
Operations, Stacks and Queues, Subroutines, Additional Instructions, Encoding of
Machine Instructions
Text book 1: Chapter1 – 1.3, 1.4, 1.6 (1.6.1-1.6.4, 1.6.7), Chapter2 – 2.2 to
2.10
RBT: L1, L2, L3
Module 2
Input/Output Organization: Accessing I/O Devices, Interrupts – Interrupt 08
Hardware, Direct Memory Access, Buses, Interface Circuits, Standard I/O
Interfaces – PCI Bus, SCSI Bus, USB.
Text book 1: Chapter4 – 4.1, 4.2, 4.4, 4.5, 4.6, 4.7
RBT: L1, L2, L3
Module 3
Memory System: Basic Concepts, Semiconductor RAM Memories, Read Only 08
Memories, Speed, Size, and Cost, Cache Memories – Mapping Functions,
Replacement Algorithms, Performance Considerations.
Text book 1: Chapter5 – 5.1 to 5.4, 5.5 (5.5.1, 5.5.2), 5.6
RBT: L1, L2, L3
Module 4
Arithmetic: Numbers, Arithmetic Operations and Characters, Addition and 08
Subtraction of Signed Numbers, Design of Fast Adders, Multiplication of Positive
Numbers, Signed Operand Multiplication, Fast Multiplication, Integer Division.
Text book 1: Chapter2-2.1, Chapter6 – 6.1 to 6.6
RBT: L1, L2, L3
Module 5
Basic Processing Unit: Some Fundamental Concepts, Execution of a Complete 08
Instruction, Multiple Bus Organization, Hard-wired Control, Micro programmed
Control.
Artificial Intelligence and Machine Learning
16

Pipelining: Basic concepts of pipelining,

Text book 1: Chapter7, Chapter8 – 8.1
RBT: L1, L2, L3
Course Outcomes: The student will be able to :
• Explain the basic organization of a computer system.
• Demonstrate functioning of different sub systems, such as processor, Input/output,and
memory.
• Illustrate hardwired control and micro programmed control, pipelining, embedded and
other computing systems.
• Design and analyse simple arithmetic and logical units.
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Carl Hamacher, Zvonko Vranesic, Safwat Zaky, Computer Organization, 5th Edition,
Tata McGraw Hill, 2002. (Listed topics only from Chapters 1, 2, 4, 5, 6, 7, 8, 9
and12)
Reference Books:
th
1. William Stallings: Computer Organization & Architecture, 9 Edition, Pearson, 2015.

Artificial Intelligence and Machine Learning

SOFTWARE ENGINEERING
(Effective from the academic year 2018 -2019)
SEMESTER – III
Course Code 18CS35 CIE Marks 40
Number of Contact Hours/Week 3:0:0 SEE Marks 60
Total Number of Contact Hours 40 Exam Hours 03
CREDITS –3
Course Learning Objectives: This course (18CS35) will enable students to:
• Outline software engineering principles and activities involved in building large
software programs.Identify ethical and professional issues and explain why they are
of concern to software engineers.
• Explain the fundamentals of object oriented concepts
• Describe the process of requirements gathering, requirements classification,
requirements specification and requirements validation. Differentiate system models,
use UML diagrams and apply design patterns.
• Discuss the distinctions between validation testing and defect testing.
• Recognize the importance of software maintenance and describe the intricacies
involved in software evolution.Apply estimation techniques, schedule project
activities and compute pricing.
• Identify software quality parameters and quantify software using measurements and
metrics. List software quality standards and outline the practices involved.
Module 1 Contact
Hours
Introduction: Software Crisis, Need for Software Engineering. Professional 08
Software Development, Software Engineering Ethics. Case Studies.
Software Processes: Models: Waterfall Model (Sec 2.1.1), Incremental Model
(Sec 2.1.2) and Spiral Model (Sec 2.1.3). Process activities.
Requirements Engineering: Requirements Engineering Processes (Chap 4).
Requirements Elicitation and Analysis (Sec 4.5). Functional and non-functional
requirements (Sec 4.1). The software Requirements Document (Sec 4.2).
Requirements Specification (Sec 4.3). Requirements validation (Sec 4.6).
Requirements Management (Sec 4.7).
RBT: L1, L2, L3
Module 2
What is Object orientation? What is OO development? OO Themes; Evidence for 08
usefulness of OO development; OO modelling history. Modelling as Design
technique: Modelling; abstraction; The Three models. Introduction, Modelling
Concepts and Class Modelling: What is Object orientation? What is OO
development? OO Themes; Evidence for usefulness of OO development; OO
modelling history. Modelling as Design technique: Modelling; abstraction; The
Three models. Class Modelling: Object and Class Concept; Link and associations
concepts; Generalization and Inheritance; A sample class model; Navigation of
class models;
Textbook 2: Ch 1,2,3.
RBT: L1, L2 L3
Module 3
System Models: Context models (Sec 5.1). Interaction models (Sec 5.2). 08
Structural models (Sec 5.3). Behavioral models (Sec 5.4). Model-driven

Artificial Intelligence and Machine Learning

engineering (Sec 5.5).

Design and Implementation: Introduction to RUP (Sec 2.4), Design Principles
(Chap 7). Object-oriented design using the UML (Sec 7.1). Design patterns (Sec
7.2). Implementation issues (Sec 7.3). Open source development (Sec 7.4).
RBT: L1, L2, L3

Module 4
Software Testing: Development testing (Sec 8.1), Test-driven development (Sec 08
8.2), Release testing (Sec 8.3), User testing (Sec 8.4). Test Automation (Page no
212).
Software Evolution: Evolution processes (Sec 9.1). Program evolution dynamics
(Sec 9.2). Software maintenance (Sec 9.3). Legacy system management (Sec 9.4).
RBT: L1, L2, L3
Module 5
Project Planning: Software pricing (Sec 23.1). Plan-driven development (Sec 08
23.2). Project scheduling (Sec 23.3): Estimation techniques (Sec 23.5). Quality
management: Software quality (Sec 24.1). Reviews and inspections (Sec 24.3).
Software measurement and metrics (Sec 24.4). Software standards (Sec 24.2)
RBT: L1, L2, L3
Course Outcomes: The student will be able to :
• Design a software system, component, or process to meet desired needs within
realistic constraints.
• Assess professional and ethical responsibility
• Function on multi-disciplinary teams
• Use the techniques, skills, and modern engineering tools necessary for engineering
practice
• Analyze, design, implement, verify, validate, implement, apply, and maintain
software systems or parts of software systems
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Ian Sommerville: Software Engineering, 9th Edition, Pearson Education, 2012.
(Listed topics only from Chapters 1,2,3,4, 5, 7, 8, 9, 23, and 24)
2. Michael Blaha, James Rumbaugh: Object Oriented Modelling and Design with
UML,2nd Edition, Pearson Education,2005.
Reference Books:
1. Roger S. Pressman: Software Engineering-A Practitioners approach, 7th Edition, Tata
McGraw Hill.
2. Pankaj Jalote: An Integrated Approach to Software Engineering, Wiley India

Artificial Intelligence and Machine Learning

DISCRETE MATHEMATICAL STRUCTURES

(Effective from the academic year 2018 -2019)
SEMESTER – III
Course Code 18CS36 CIE Marks 40
Number of Contact Hours/Week 3:0:0 SEE Marks 60
Total Number of Contact Hours 40 Exam Hours 03
CREDITS –3
Course Learning Objectives: This course (18CS36) will enable students to:
• Provide theoretical foundations of computer science to perceive other courses in the
programme.
• Illustrate applications of discrete structures: logic, relations, functions, set theory and
counting.
• Describe different mathematical proof techniques,
• Illustrate the importance of graph theory in computer science
Module 1 Contact
Hours
Fundamentals of Logic: Basic Connectives and Truth Tables, Logic Equivalence 08
– The Laws of Logic, Logical Implication – Rules of Inference. Fundamentals of
Logic contd.: The Use of Quantifiers, Quantifiers, Definitions and the Proofs of
Theorems.
Text book 1: Chapter2
RBT: L1, L2, L3
Module 2
Properties of the Integers: The Well Ordering Principle – Mathematical 08
Induction,
Fundamental Principles of Counting: The Rules of Sum and Product,
Permutations, Combinations – The Binomial Theorem, Combinations with
Repetition.
Text book 1: Chapter4 – 4.1, Chapter1
RBT: L1, L2, L3
Module 3
Relations and Functions: Cartesian Products and Relations, Functions – Plain 08
and One-to-One, Onto Functions. The Pigeon-hole Principle, Function
Composition and Inverse Functions.
Relations: Properties of Relations, Computer Recognition – Zero-One Matrices
and Directed Graphs, Partial Orders – Hasse Diagrams, Equivalence Relations
and Partitions.
Text book 1: Chapter5 , Chapter7 – 7.1 to 7.4
RBT: L1, L2, L3
Module 4
The Principle of Inclusion and Exclusion: The Principle of Inclusion and 08
Exclusion, Generalizations of the Principle, Derangements – Nothing is in its
Right Place, Rook Polynomials.
Recurrence Relations: First Order Linear Recurrence Relation, The Second
Order Linear Homogeneous Recurrence Relation with Constant Coefficients.
Text book 1: Chapter8 – 8.1 to 8.4, Chapter10 – 10.1, 10.2
RBT: L1, L2, L3
Module 5
Introduction to Graph Theory: Definitions and Examples, Sub graphs, 08
Complements, and Graph Isomorphism,
Trees: Definitions, Properties, and Examples, Routed Trees, Trees and Sorting,
Artificial Intelligence and Machine Learning
20

Weighted Trees and Prefix Codes

Text book 1: Chapter11 – 11.1 to 11.2 Chapter12 – 12.1 to 12.4
RBT: L1, L2, L3
Course Outcomes: The student will be able to :
• Use propositional and predicate logic in knowledge representation and truth
verification.
• Demonstrate the application of discrete structures in different fields of computer
science.
• Solve problems using recurrence relations and generating functions.
• Application of different mathematical proofs techniques in proving theorems in the
courses.
• Compare graphs, trees and their applications.
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Ralph P. Grimaldi: Discrete and Combinatorial Mathematics, 5th Edition, Pearson
Education. 2004.
Reference Books:
1. Basavaraj S Anami and Venakanna S Madalli: Discrete Mathematics – A Concept
based approach, Universities Press, 2016
2. Kenneth H. Rosen: Discrete Mathematics and its Applications, 6th Edition, McGraw
Hill, 2007.
3. Jayant Ganguly: A Treatise on Discrete Mathematical Structures, Sanguine-Pearson,
2010.
4. D.S. Malik and M.K. Sen: Discrete Mathematical Structures: Theory and
Applications, Thomson, 2004.
5. Thomas Koshy: Discrete Mathematics with Applications, Elsevier, 2005, Reprint
2008.

Artificial Intelligence and Machine Learning

ANALOG AND DIGITAL ELECTRONICS LABORATORY

(Effective from the academic year 2018 -2019)
SEMESTER – III
Course Code 18CSL37 CIE Marks 40
Number of Contact Hours/Week 0:2:2 SEE Marks 60
Total Number of Lab Contact Hours 36 Exam Hours 03
Credits – 2
Course Learning Objectives: This course (18CSL37) will enable students to:
This laboratory course enable students to get practical experience in design, assembly and
evaluation/testing of
• Analog components and circuits including Operational Amplifier, Timer, etc.
• Combinational logic circuits.
• Flip - Flops and their operations
• Counters and registers using flip-flops.
• Synchronous and Asynchronous sequential circuits.
• A/D and D/A converters
Descriptions (if any):
• Simulation packages preferred: Multisim, Modelsim, PSpice or any other relevant.
• For Part A (Analog Electronic Circuits) students must trace the wave form on Tracing
sheet / Graph sheet and label trace.
• Continuous evaluation by the faculty must be carried by including performance of a
student in both hardware implementation and simulation (if any) for the given circuit.
• A batch not exceeding 4 must be formed for conducting the experiment. For
simulation individual student must execute the program.
Laboratory Programs:
PART A (Analog Electronic Circuits)
1. Design an astable multivibrator ciruit for three cases of duty cycle (50%, <50%
and >50%) using NE 555 timer IC. Simulate the same for any one duty cycle.
2. Using ua 741 Opamp, design a 1 kHz Relaxation Oscillator with 50% duty cycle.
And simulate the same.
3. Using ua 741 opamap, design a window comparate for any given UTP and LTP.
And simulate the same.
PART B (Digital Electronic Circuits)
4. Design and implement Half adder, Full Adder, Half Subtractor, Full Subtractor
using basic gates. And implement the same in HDL.
5. Given a 4-variable logic expression, simplify it using appropriate technique and
realize the simplified logic expression using 8:1 multiplexer IC. And implement
the same in HDL.
6. Realize a J-K Master / Slave Flip-Flop using NAND gates and verify its truth
table. And implement the same in HDL.
7. Design and implement code converter I)Binary to Gray (II) Gray to Binary Code
using basic gates.
8. Design and implement a mod-n (n<8) synchronous up counter using J-K Flip-Flop
ICs and demonstrate its working.
9. Design and implement an asynchronous counter using decade counter IC to count
up from 0 to n (n<=9) and demonstrate on 7-segment display (using IC-7447)
Laboratory Outcomes: The student should be able to:
• Use appropriate design equations / methods to design the given circuit.
• Examine and verify the design of both analog and digital circuits using simulators.
• Make us of electronic components, ICs, instruments and tools for design and testing
Artificial Intelligence and Machine Learning
22

of circuits for the given the appropriate inputs.

• Compile a laboratory journal which includes; aim,
tool/instruments/software/components used, design equations used and designs,
schematics, program listing, procedure followed, relevant theory, results as graphs
and tables, interpreting and concluding the findings.
Conduct of Practical Examination:
• Experiment distribution
o For laboratories having only one part: Students are allowed to pick one
experiment from the lot with equal opportunity.
o For laboratories having PART A and PART B: Students are allowed to pick
one experiment from PART A and one experiment from PART B, with equal
opportunity.
• Change of experiment is allowed only once and marks allotted for procedure to be
made zero of the changed part only.
• Marks Distribution (Courseed to change in accoradance with university regulations)
a) For laboratories having only one part – Procedure + Execution + Viva-Voce:
15+70+15 = 100 Marks
b) For laboratories having PART A and PART B
i. Part A – Procedure + Execution + Viva = 6 + 28 + 6 = 40 Marks
ii. Part B – Procedure + Execution + Viva = 9 + 42 + 9 = 60 Marks

Artificial Intelligence and Machine Learning

DATA STRUCTURES LABORATORY

(Effective from the academic year 2018 -2019)
SEMESTER – III
Course Code 18CSL38 CIE Marks 40
Number of Contact Hours/Week 0:2:2 SEE Marks 60
Total Number of Lab Contact Hours 36 Exam Hours 03
Credits – 2
Course Learning Objectives: This course (18CSL38) will enable students to:
This laboratory course enable students to get practical experience in design, develop,
implement, analyze and evaluation/testing of
• Asymptotic performance of algorithms.
• Linear data structures and their applications such as stacks, queues and lists
• Non-Linear data structures and their applications such as trees and graphs
• Sorting and searching algorithms
Descriptions (if any):
• Implement all the programs in ‘C / C++’ Programming Language and Linux /
Windows as OS.
Programs List:
1. Design, Develop and Implement a menu driven Program in C for the following
array operations.
a. Creating an array of N Integer Elements
b. Display of array Elements with Suitable Headings
c. Inserting an Element (ELEM) at a given valid Position (POS)
d. Deleting an Element at a given valid Position (POS)
e. Exit.
Support the program with functions for each of the above operations.
2. Design, Develop and Implement a Program in C for the following operations on
Strings.
a. Read a main String (STR), a Pattern String (PAT) and a Replace String
(REP)
b. Perform Pattern Matching Operation: Find and Replace all occurrences of
PAT in STR with REP if PAT exists in STR. Report suitable messages in
case PAT does not exist in STR
Support the program with functions for each of the above operations. Don't use
Built-in functions.
3. Design, Develop and Implement a menu driven Program in C for the following
operations on STACK of Integers (Array Implementation of Stack with maximum
size MAX)
a. Push an Element on to Stack
b. Pop an Element from Stack
c. Demonstrate how Stack can be used to check Palindrome
d. Demonstrate Overflow and Underflow situations on Stack
e. Display the status of Stack
f. Exit
Support the program with appropriate functions for each of the above operations

4. Design, Develop and Implement a Program in C for converting an Infix

Expression to Postfix Expression. Program should support for both parenthesized
and free parenthesized expressions with the operators: +, -, *, /, % (Remainder), ^
(Power) and alphanumeric operands.
5. Design, Develop and Implement a Program in C for the following Stack
Artificial Intelligence and Machine Learning
24

Applications
a. Evaluation of Suffix expression with single digit operands and operators:
+, -, *, /, %, ^
b. Solving Tower of Hanoi problem with n disks
6. Design, Develop and Implement a menu driven Program in C for the following
operations on Circular QUEUE of Characters (Array Implementation of Queue
with maximum size MAX)
a. Insert an Element on to Circular QUEUE
b. Delete an Element from Circular QUEUE
c. Demonstrate Overflow and Underflow situations on Circular QUEUE
d. Display the status of Circular QUEUE
e. Exit
Support the program with appropriate functions for each of the above operations
7. Design, Develop and Implement a menu driven Program in C for the following
operations on Singly Linked List (SLL) of Student Data with the fields: USN,
Name, Programme, Sem, PhNo
a. Create a SLL of N Students Data by using front insertion.
b. Display the status of SLL and count the number of nodes in it
c. Perform Insertion / Deletion at End of SLL
d. Perform Insertion / Deletion at Front of SLL(Demonstration of stack)
e. Exit
8. Design, Develop and Implement a menu driven Program in C for the following
operations on Doubly Linked List (DLL) of Employee Data with the fields: SSN,
Name, Dept, Designation, Sal, PhNo
a. Create a DLL of N Employees Data by using end insertion.
b. Display the status of DLL and count the number of nodes in it
c. Perform Insertion and Deletion at End of DLL
d. Perform Insertion and Deletion at Front of DLL
e. Demonstrate how this DLL can be used as Double Ended Queue.
f. Exit
9. Design, Develop and Implement a Program in C for the following operationson
Singly Circular Linked List (SCLL) with header nodes
a. Represent and Evaluate a Polynomial P(x,y,z) = 6x2y2z-
4yz5+3x3yz+2xy5z-2xyz3
b. Find the sum of two polynomials POLY1(x,y,z) and POLY2(x,y,z) and
store the result in POLYSUM(x,y,z)
Support the program with appropriate functions for each of the above operations
10. Design, Develop and Implement a menu driven Program in C for the following
operations on Binary Search Tree (BST) of Integers .
a. Create a BST of N Integers: 6, 9, 5, 2, 8, 15, 24, 14, 7, 8, 5, 2
b. Traverse the BST in Inorder, Preorder and Post Order
c. Search the BST for a given element (KEY) and report the appropriate
message
d. Exit
11. Design, Develop and Implement a Program in C for the following operations on
Graph(G) of Cities
a. Create a Graph of N cities using Adjacency Matrix.
b. Print all the nodes reachable from a given starting node in a digraph using
DFS/BFS method
12. Given a File of N employee records with a set K of Keys (4-digit) which uniquely
determine the records in file F. Assume that file F is maintained in memory by a

Artificial Intelligence and Machine Learning

Hash Table (HT) of m memory locations with L as the set of memory addresses
(2-digit) of locations in HT. Let the keys in K and addresses in L are Integers.
Design and develop a Program in C that uses Hash function H: K →L as H(K)=K
mod m (remainder method), and implement hashing technique to map a given key
K to the address space L. Resolve the collision (if any) using linear probing.
Laboratory Outcomes: The student should be able to:
• Analyze and Compare various linear and non-linear data structures
• Code, debug and demonstrate the working nature of different types of data structures
and their applications
• Implement, analyze and evaluate the searching and sorting algorithms
• Choose the appropriate data structure for solving real world problems
Conduct of Practical Examination:
• Experiment distribution
o For laboratories having only one part: Students are allowed to pick one
experiment from the lot with equal opportunity.
o For laboratories having PART A and PART B: Students are allowed to pick
one experiment from PART A and one experiment from PART B, with equal
opportunity.
• Change of experiment is allowed only once and marks allotted for procedure to be
made zero of the changed part only.
• Marks Distribution (Courseed to change in accoradance with university regulations)
c) For laboratories having only one part – Procedure + Execution + Viva-Voce:
15+70+15 = 100 Marks
d) For laboratories having PART A and PART B
i. Part A – Procedure + Execution + Viva = 6 + 28 + 6 = 40 Marks
ii. Part B – Procedure + Execution + Viva = 9 + 42 + 9 = 60 Marks

Artificial Intelligence and Machine Learning

B. E. Common to all Programmes 26
Outcome Based Education (OBE) and Choice Based Credit System (CBCS)
SEMESTER –II / III / IV
Aadalitha Kannada
Course Code 18KAK28/39/49
Teaching Hours/Week (L:T:P) (0:2:0) CIE Marks 100
Credits 01
DqÀ½vÀ PÀ£ÀßqÀ PÀ°PÉAiÀÄ GzÉÝÃ±ÀUÀ¼ÀÄ:
• ¥ÀzÀ« «zÁåyð¼ÁVgÀÄªÀÅzÀjAzÀ DqÀ½vÀ PÀ£ÀßqÀzÀ ¥ÀjZÀAiÀÄ
ªÀiÁrPÉÆqÀÄªÀÅzÀÄ.
• «zÁåyðUÀ¼À°è PÀ£ÀßqÀ ¨sÁµÉAiÀÄ ªÁåPÀgÀtzÀ §UÉÎ CjªÀÅ ªÀÄÆr¸ÀÄªÀÅzÀÄ.
• PÀ£ÀßqÀ ¨sÁµÁ gÀZÀ£ÉAiÀÄ°è£À ¤AiÀÄªÀÄUÀ¼À£ÀÄß ¥ÀjZÀ¬Ä¸ÀÄªÀÅzÀÄ.
• PÀ£ÀßqÀ ¨sÁµÁ §gÀºÀzÀ°è PÀAqÀÄ§gÀÄªÀ zÉÆÃµÀUÀ¼ÀÄ ºÁUÀÆ CªÀÅUÀ¼À
¤ªÁgÀuÉ. ªÀÄvÀÄÛ ¯ÉÃR£À aºÉßUÀ¼À£ÀÄß ¥ÀjZÀ¬Ä¸ÀÄªÀÅzÀÄ.
• ¸ÁªÀiÁ£Àå CfðUÀ¼ÀÄ, ¸ÀPÁðj ªÀÄvÀÄÛ CgÉ ¸ÀPÁðj ¥ÀvÀæªÀåªÀºÁgÀzÀ §UÉÎ
CjªÀÅ ªÀÄÆr¸ÀÄªÀÅzÀÄ.
• ¨sÁµÁAvÀgÀ ªÀÄvÀÄÛ ¥Àæ§AzsÀ gÀZÀ£É §UÉÎ C¸ÀQÛ ªÀÄÆr¸ÀÄªÀÅzÀÄ.
• PÀ£ÀßqÀ ¨sÁµÁ¨sÁå¸À ªÀÄvÀÄÛ ¸ÁªÀiÁ£Àå PÀ£ÀßqÀ ºÁUÀÆ DqÀ½vÀ PÀ£ÀßqÀzÀ
¥ÀzÀUÀ¼À ¥ÀjZÀAiÀÄ ªÀiÁrPÉÆqÀÄªÀÅzÀÄ.
¥Àj«r (¥ÀoÀå¥ÀÄ¸ÀÛPÀzÀ°ègÀÄªÀ «µÀAiÀÄUÀ¼À ¥ÀnÖ)
CzsÁåAiÀÄ – 1 PÀ£ÀßqÀ¨sÁµÉ – ¸ÀAQë¥ÀÛ «ªÀgÀuÉ.
CzsÁåAiÀÄ – 2 ¨sÁµÁ ¥ÀæAiÉÆÃUÀzÀ¯ÁèUÀÄªÀ ¯ÉÆÃ¥ÀzÉÆÃµÀUÀ¼ÀÄ ªÀÄvÀÄÛ
CªÀÅUÀ¼À ¤ªÁgÀuÉ.
CzsÁåAiÀÄ – 3 ¯ÉÃR£À aºÉßUÀ¼ÀÄ ªÀÄvÀÄÛ CªÀÅUÀ¼À G¥ÀAiÉÆÃUÀ.
CzsÁåAiÀÄ – 4 ¥ÀvÀæ ªÀåªÀºÁgÀ.
CzsÁåAiÀÄ – 5 DqÀ½vÀ ¥ÀvÀæUÀ¼ÀÄ.
CzsÁåAiÀÄ – 6 ¸ÀPÁðgÀzÀ DzÉÃ±À ¥ÀvÀæUÀ¼ÀÄ.
CzsÁåAiÀÄ – 7 ¸ÀAQë¥ÀÛ ¥Àæ§AzsÀ gÀZÀ£É (¦æ¸ÉÊ¸ï gÉÊnAUï), ¥Àæ§AzsÀ ªÀÄvÀÄÛ
¨sÁµÁAvÀgÀ.
CzsÁåAiÀÄ – 8 PÀ£ÀßqÀ ±À§Ý¸ÀAUÀæºÀ.
CzsÁåAiÀÄ – 9 PÀA¥ÀÆålgï ºÁUÀÆ ªÀiÁ»w vÀAvÀæeÁÕ£À.
CzsÁåAiÀÄ – 10 ¥Áj¨sÁ¶PÀ DqÀ½vÀ PÀ£ÀßqÀ ¥ÀzÀUÀ¼ÀÄ ªÀÄvÀÄÛ vÁAwæPÀ/
PÀA¥ÀÆålgï ¥Áj¨sÁ¶PÀ ¥ÀzÀUÀ¼ÀÄ.
DqÀ½vÀ PÀ£ÀßqÀ PÀ°PÉAiÀÄ ¥sÀ°vÁA±ÀÀUÀ¼ÀÄ:
• DqÀ½vÀ ¨sÁµÉ PÀ£ÀßqÀzÀ ¥ÀjZÀAiÀÄªÁUÀÄvÀÛzÉ.
• «zÁåyðUÀ¼À°è PÀ£ÀßqÀ ¨sÁµÉAiÀÄ ªÁåPÀgÀtzÀ §UÉÎ CjªÀÅ ªÀÄÆqÀÄvÀÛzÉ.
• PÀ£ÀßqÀ ¨sÁµÁ gÀZÀ£ÉAiÀÄ°è£À ¤AiÀÄªÀÄUÀ¼ÀÄ ªÀÄvÀÄÛ ¯ÉÃR£À
aºÉßUÀ¼ÀÄ ¥ÀjZÀ¬Ä¸À®àqÀÄvÀÛªÉ.
• ¸ÁªÀiÁ£Àå CfðUÀ¼ÀÄ, ¸ÀPÁðj ªÀÄvÀÄÛ CgÉ ¸ÀPÁðj ¥ÀvÀæªÀåªÀºÁgÀzÀ §UÉÎ
CjªÀÅ ªÀÄÆqÀÄvÀÛzÉ.
• ¨sÁµÁAvÀgÀ ªÀÄvÀÄÛ ¥Àæ§AzsÀ gÀZÀ£É §UÉÎ C¸ÀQÛ ªÀÄÆqÀÄvÀÛzÉ.
• PÀ£ÀßqÀ ¨sÁµÁ¨sÁå¸À ªÀÄvÀÄÛ ¸ÁªÀiÁ£Àå PÀ£ÀßqÀ ºÁUÀÆ DqÀ½vÀ PÀ£ÀßqÀzÀ
¥ÀzÀUÀ¼ÀÄ ¥ÀjZÀ¬Ä¸À®àqÀÄvÀÛªÉ.
¥ÀjÃPÉëAiÀÄ «zsÁ£À : ¤gÀAvÀgÀ DAvÀjPÀ ªÀiË®åªÀiÁ¥À£À - CIE (Continuous Internal
Evaluation):
PÁ¯ÉÃdÄ ªÀÄlÖzÀ°èAiÉÄ DAvÀjPÀ ¥ÀjÃPÉëAiÀÄ£ÀÄß 100 CAPÀUÀ½UÉ
«±Àé«zÁå®AiÀÄzÀ
¤AiÀÄªÀÄUÀ¼ÀÄ ªÀÄvÀÄÛ ¤zÉðÃ±À£ÀzÀAvÉ £ÀqÉ¸ÀvÀPÀÌzÀÄÝ.
¥ÀoÀå¥ÀÄ¸ÀÛPÀ : DqÀ½vÀ PÀ£ÀßqÀ ¥ÀoÀå ¥ÀÄ¸ÀÛPÀ (Kannada for Administration)
¸ÀÀA¥ÁzÀPÀgÀÄ
qÁ. J¯ï. wªÉÄäÃ±À
¥ÉÆæ. «. PÉÃ±ÀªÀªÀÄÆwð
Artificial Intelligence and Machine Learning
27

¥ÀæPÀluÉ : ¥Àæ¸ÁgÁAUÀ, «±ÉéÃ±ÀégÀAiÀÄå vÁAwæPÀ «±Àé«zÁå®AiÀÄ,

¨É¼ÀUÁ«.

B. E. Common to all Programmes

Outcome Based Education (OBE) and Choice Based Credit System (CBCS)
SEMESTER –II & III/IV
Vyavaharika Kannada
Course Code 18KVK28/39/49
Teaching Hours/Week (L:T:P) (0:2:0) CIE Marks 100
Credits 01
Course Learning Objectives:
The course will enable the students to understand Kannada and communicate in Kannada language.
Table of Contents:
Chapter - 1: Vyavaharika kannada – Parichaya (Introduction to Vyavaharika Kannada).
Chapter - 2: Kannada Aksharamale haagu uchcharane ( Kannada Alpabets and Pronunciation).
Chapter - 3: Sambhashanegaagi Kannada Padagalu (Kannada Vocabulary for Communication).
Chapter - 4: Kannada Grammar in Conversations (Sambhashaneyalli Kannada Vyakarana).
Chapter - 5: Activities in Kannada.
Course Outcomes:
At the end of the course, the student will be able to understand Kannada and communicate in
Kannada
language.
¥ÀjÃPÉëAiÀÄ «zsÁ£À : ¤gÀAvÀgÀ DAvÀjPÀ ªÀiË®åªÀiÁ¥À£À - CIE (Continuous Internal
Evaluation):
PÁ¯ÉÃdÄ ªÀÄlÖzÀ°èAiÉÄ DAvÀjPÀ ¥ÀjÃPÉëAiÀÄ£ÀÄß 100 CAPÀUÀ½UÉ
«±Àé«zÁå®AiÀÄzÀ
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Artificial Intelligence and Machine Learning

B. E. Common to all Programmes

Outcome Based Education (OBE) and Choice Based Credit System (CBCS)
SEMESTER - III
CONSTITUTION OF INDIA, PROFESSIONAL ETHICS AND CYBER LAW (CPC)
Course Code 18CPC39/49 CIE Marks 40
Teaching Hours/Week (L:T:P) (1:0:0) SEE Marks 60
Credits 01 Exam Hours 02
Course Learning Objectives: To
• know the fundamental political codes, structure, procedures, powers, and duties of Indian
government institutions, fundamental rights, directive principles, and the duties of citizens
• Understand engineering ethics and their responsibilities; identify their individual roles and
ethical responsibilities towards society.
• Know about the cybercrimes and cyber laws for cyber safety measures.
Module-1
Introduction to Indian Constitution:
The Necessity of the Constitution, The Societies before and after the Constitution adoption.
Introduction to the Indian constitution, The Making of the Constitution, The Role of the Constituent
Assembly - Preamble and Salient features of the Constitution of India. Fundamental Rights and its
Restriction and limitations in different Complex Situations. Directive Principles of State Policy
(DPSP) and its present relevance in our society with examples. Fundamental Duties and its
Scope and significance in Nation building.
Module-2
Union Executive and State Executive:
Parliamentary System, Federal System, Centre-State Relations. Union Executive – President, Prime
Minister, Union Cabinet, Parliament - LS and RS, Parliamentary Committees, Important
Parliamentary Terminologies. Supreme Court of India, Judicial Reviews and Judicial Activism. State
Executives – Governor, Chief Minister, State Cabinet, State Legislature, High Court and
Subordinate Courts, Special Provisions (Articles 370.371,371J) for some States.
Module-3
Elections, Amendments and Emergency Provisions:
Elections, Electoral Process, and Election Commission of India, Election Laws. Amendments -
Methods in Constitutional Amendments (How and Why) and Important Constitutional
Amendments. Amendments – 7,9,10,12,42,44, 61, 73,74, ,75, 86, and 91,94,95,100,101,118 and
some important Case Studies. Emergency Provisions, types of Emergencies and its consequences.
Constitutional special provisions:
Special Provisions for SC and ST, OBC, Women, Children and Backward Classes.
Module-4
Professional / Engineering Ethics:
Scope & Aims of Engineering & Professional Ethics - Business Ethics, Corporate Ethics, Personal
Ethics. Engineering and Professionalism, Positive and Negative Faces of Engineering Ethics, Code
of Ethics as defined in the website of Institution of Engineers (India): Profession, Professionalism,
and Professional Responsibility. Clash of Ethics, Conflicts of Interest. Responsibilities in Engineering
Responsibilities in Engineering and Engineering Standards, the impediments to Responsibility. Trust
and Reliability in Engineering, IPRs (Intellectual Property Rights), Risks, Safety and liability in
Engineering
Module-5
Internet Laws, Cyber Crimes and Cyber Laws:
Internet and Need for Cyber Laws, Modes of Regulation of Internet, Types of cyber terror
capability, Net neutrality, Types of Cyber Crimes, India and cyber law, Cyber Crimes and the
Artificial Intelligence and Machine Learning
29

information Technology Act 2000, Internet Censorship. Cybercrimes and enforcement agencies.
Course Outcomes: On completion of this course, students will be able to,
CO 1: Have constitutional knowledge and legal literacy.
CO 2: Understand Engineering and Professional ethics and responsibilities of Engineers.
CO 3: Understand the the cybercrimes and cyber laws for cyber safety measures.
Question paper pattern for SEE and CIE:
• The SEE question paper will be set for 100 marks and the marks scored by the students will
proportionately be reduced to 60. The pattern of the question paper will be objective type
(MCQ).
• For the award of 40 CIE marks, refer the University regulations 2018.
Sl. Title of the Book Name of the Name of the Edition and
No. Author/s Publisher Year
Textbook/s
1 Constitution of India, Shubham Singles, 2018
Professional Ethics and Charles E. Haries, Cengage
Human Rights and et al Learning India
2 Cyber Security and Cyber Alfred Basta and et Cengage 2018
Laws al Learning India
Reference Books
3 Introduction to the Durga Das Basu Prentice –Hall, 2008.
Constitution of India
4 Engineering Ethics M. Govindarajan, Prentice –Hall, 2004
S. Natarajan, V. S.
Senthilkumar

Artificial Intelligence and Machine Learning

B. E. Common to all Programmes

Outcome Based Education (OBE) and Choice Based Credit System (CBCS)
SEMESTER - III
ADDITIONAL MATHEMATICS – I
(Mandatory Learning Course: Common to All Programmes)
(A Bridge course for Lateral Entry students under Diploma quota to BE/B. Tech. programmes)
Course Code 18MATDIP31 CIE Marks 40
Teaching Hours/Week (L:T:P) (2:2:0) SEE Marks 60
Credits 0 Exam Hours 03
Course Learning Objectives:
• To provide basic concepts of complex trigonometry, vector algebra, differential and integral
calculus.
• To provide an insight into vector differentiation and first order ODE’s.
Module-1
Complex Trigonometry: Complex Numbers: Definitions and properties. Modulus and amplitude
of a complex number, Argand’s diagram, De-Moivre’s theorem (without proof).
Vector Algebra: Scalar and vectors. Addition and subtraction and multiplication of vectors- Dot and
Cross products, problems.
Module-2
Differential Calculus: Review of successive differentiation-illustrative examples. Maclaurin’s series
expansions-Illustrative examples. Partial Differentiation: Euler’s theorem-problems on first order
derivatives only. Total derivatives-differentiation of composite functions. Jacobians of order two-
Module-3
Vector Differentiation: Differentiation of vector functions. Velocity and acceleration of a particle
moving on a space curve. Scalar and vector point functions. Gradient, Divergence, Curl-simple
problems. Solenoidal and irrotational vector fields-Problems.
Module-4
Integral Calculus: Review of elementary integral calculus. Reduction formulae for sinnx, cosnx (with
proof) and sinmxcosnx (without proof) and evaluation of these with standard limits-Examples. Double
and triple integrals-Simple examples.
Module-5
Ordinary differential equations (ODE’s. Introduction-solutions of first order and first-degree
differential equations: exact, linear differential equations. Equations reducible to exact and
Bernoulli’s equation.
Course Outcomes: At the end of the course the student will be able to:
• CO1: Apply concepts of complex numbers and vector algebra to analyze the problems arising
in related area.
• CO2: Use derivatives and partial derivatives to calculate rate of change of multivariate
functions.
• CO3: Analyze position, velocity and acceleration in two and three dimensions of vector
valued functions.
• CO4: Learn techniques of integration including the evaluation of double and triple integrals.
• CO5: Identify and solve first order ordinary differential equations.
Question paper pattern:
• The question paper will have ten full questions carrying equal marks.
• Each full question will be for 20 marks.
• There will be two full questions (with a maximum of four sub- questions) from each module.
• Each full question will have sub- question covering all the topics under a module.

Artificial Intelligence and Machine Learning

• The students will have to answer five full questions, selecting one full question from each
module.
Sl
Name of the
No Title of the Book Name of the Edition and Year
Author/s
Publisher
Textbook
1 Higher Engineering Mathematics B. S. Grewal Khanna Publishers 43rd Edition, 2015
Reference Books
1 Advanced Engineering E. Kreyszig John Wiley & Sons 10th Edition, 2015
Mathematics
2 Engineering Mathematics N. P .Bali and Laxmi Publishers 7th Edition, 2007
Manish Goyal
3 Engineering Mathematics Vol. I Rohit Cengage Learning 1st Edition, 2015
Khurana

Artificial Intelligence and Machine Learning

B. E. COMMON TO ALL PROGRAMMES

Choice Based Credit System (CBCS) and Outcome Based Education (OBE)
SEMESTER - IV
COMPLEX ANALYSIS, PROBABILITY AND STATISTICAL METHODS
(Common to all programmes)
[As per Choice Based Credit System (CBCS) scheme]
Course Code 18MAT41 CIE Marks 40
Teaching Hours/Week (L:T:P) (2:2:0) SEE Marks 60
Credits 03 Exam Hours 03
Course Learning Objectives:
• To provide an insight into applications of complex variables, conformal mapping and special functions
arising in potential theory, quantum mechanics, heat conduction and field theory.
• To develop probability distribution of discrete, continuous random variables and joint probability
distribution occurring in digital signal processing, design engineering and microwave engineering.
Module-1
Calculus of complex functions: Review of function of a complex variable, limits, continuity, and
differentiability. Analytic functions: Cauchy-Riemann equations in Cartesian and polar forms and
consequences.
Construction of analytic functions: Milne-Thomson method-Problems.
Module-2
Conformal transformations: Introduction. Discussion of transformations:𝑤𝑤 = 𝑍𝑍 2 , 𝑤𝑤 = 𝑒𝑒 𝑧𝑧 , 𝑤𝑤 = 𝑧𝑧 +
1
, (𝑧𝑧 ≠ 0).Bilinear transformations- Problems.
𝑧𝑧
Complex integration: Line integral of a complex function-Cauchy’s theorem and Cauchy’s integral formula
and problems.
Module-3
Probability Distributions: Review of basic probability theory. Random variables (discrete and continuous),
probability mass/density functions. Binomial, Poisson, exponential and normal distributions- problems (No
derivation for mean and standard deviation)-Illustrative examples.
Module-4
Statistical Methods: Correlation and regression-Karl Pearson’s coefficient of correlation and rank correlation
-problems. Regression analysis- lines of regression –problems.
Curve Fitting: Curve fitting by the method of least squares- fitting the curves of the form-
𝑦𝑦 = 𝑎𝑎𝑎𝑎 + 𝑏𝑏, 𝑦𝑦 = 𝑎𝑎𝑥𝑥 𝑏𝑏 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 𝑎𝑎𝑥𝑥 2 + 𝑏𝑏𝑏𝑏 + 𝑐𝑐.
Module-5
Joint probability distribution: Joint Probability distribution for two discrete random variables, expectation
and covariance.
Sampling Theory: Introduction to sampling distributions, standard error, Type-I and Type-II errors. Test of
hypothesis for means, student’s t-distribution, Chi-square distribution as a test of goodness of fit.
Course Outcomes: At the end of the course the student will be able to:
• Use the concepts of analytic function and complex potentials to solve the problems arising in
electromagnetic field theory.
• Utilize conformal transformation and complex integral arising in aerofoil theory, fluid flow
visualization and image processing.
• Apply discrete and continuous probability distributions in analyzing the probability models arising in
engineering field.
• Make use of the correlation and regression analysis to fit a suitable mathematical model for the
statistical data.
• Construct joint probability distributions and demonstrate the validity of testing the hypothesis.
Question paper pattern:
• The question paper will have ten full questions carrying equal marks.
• Each full question will be for 20 marks.
• There will be two full questions (with a maximum of four sub- questions) from each module.
Sl. No. Title of the Book Name of the Name of the Edition and Year
Artificial Intelligence and Machine Learning
33

Author/s Publisher
Textbooks
1 Advanced Engineering E. Kreyszig John Wiley & Sons 10th Edition,2016
Mathematics
2 Higher Engineering B. S. Grewal Khanna Publishers 44th Edition, 2017
Mathematics
3 Engineering Mathematics Srimanta Pal et al Oxford University 3rd Edition,2016
Press
Reference Books
1 Advanced Engineering C. Ray Wylie, McGraw-Hill 6th Edition 1995
Mathematics Louis C.Barrett
2 Introductory Methods of S.S.Sastry Prentice Hall of 4th Edition 2010
Numerical Analysis India
3 Higher Engineering B. V. Ramana McGraw-Hill 11th Edition,2010
Mathematics
4 A Text Book of Engineering N. P. Bali and Laxmi Publications 2014
Mathematics Manish Goyal
5 Advanced Engineering Chandrika Prasad Khanna 2018
Mathematics and Reena Garg Publishing,
Web links and Video Lectures:
1. http://nptel.ac.in/courses.php?disciplineID=111
2. http://www.class-central.com/subject/math(MOOCs)
3. http://academicearth.org/
4. VTU EDUSAT PROGRAMME - 20

Artificial Intelligence and Machine Learning

DESIGN AND ANALYSIS OF ALGORITHMS

(Effective from the academic year 2018 -2019)
SEMESTER – IV
Course Code 18CS42 CIE Marks 40
Number of Contact Hours/Week 3:2:0 SEE Marks 60
Total Number of Contact Hours 50 Exam Hours 03
CREDITS –4
Course Learning Objectives: This course (18CS42) will enable students to:
• Explain various computational problem solving techniques.
• Apply appropriate method to solve a given problem.
• Describe various methods of algorithm analysis.
Module 1 Contact
Hours
Introduction: What is an Algorithm? (T2:1.1), Algorithm Specification (T2:1.2), 10
Analysis Framework (T1:2.1), Performance Analysis: Space complexity, Time
complexity (T2:1.3). Asymptotic Notations: Big-Oh notation (O), Omega
notation (Ω), Theta notation (Θ), and Little-oh notation (o), Mathematical analysis
of Non-Recursive and recursive Algorithms with Examples (T1:2.2, 2.3, 2.4).
Important Problem Types: Sorting, Searching, String processing, Graph
Problems, Combinatorial Problems. Fundamental Data Structures: Stacks,
Queues, Graphs, Trees, Sets and Dictionaries. (T1:1.3,1.4).

RBT: L1, L2, L3

Module 2
Divide and Conquer: General method, Binary search, Recurrence 10
equation for divide and conquer, Finding the maximum and minimum
(T2:3.1, 3.3, 3.4), Merge sort, Quick sort (T1:4.1, 4.2), Strassen’s
matrix multiplication (T2:3.8), Advantages and Disadvantages of divide
and conquer. Decrease and Conquer Approach: Topological Sort.
(T1:5.3).

RBT: L1, L2, L3

Module 3
Greedy Method: General method, Coin Change Problem, Knapsack Problem, 10
Job sequencing with deadlines (T2:4.1, 4.3, 4.5). Minimum cost spanning trees:
Prim’s Algorithm, Kruskal’s Algorithm (T1:9.1, 9.2). Single source shortest
paths: Dijkstra's Algorithm (T1:9.3). Optimal Tree problem: Huffman Trees
and Codes (T1:9.4). Transform and Conquer Approach: Heaps and Heap Sort
(T1:6.4).

RBT: L1, L2, L3

Module 4
Dynamic Programming: General method with Examples, Multistage Graphs 10
(T2:5.1, 5.2). Transitive Closure: Warshall’s Algorithm, All Pairs Shortest
Paths: Floyd's Algorithm, Optimal Binary Search Trees, Knapsack problem
((T1:8.2, 8.3, 8.4), Bellman-Ford Algorithm (T2:5.4), Travelling Sales Person
problem (T2:5.9), Reliability design (T2:5.8).

Artificial Intelligence and Machine Learning

RBT: L1, L2, L3

Module 5
Backtracking: General method (T2:7.1), N-Queens problem (T1:12.1), Sum of 10
subsets problem (T1:12.1), Graph coloring (T2:7.4), Hamiltonian cycles (T2:7.5).
Programme and Bound: Assignment Problem, Travelling Sales Person problem
(T1:12.2), 0/1 Knapsack problem (T2:8.2, T1:12.2): LC Programme and Bound
solution (T2:8.2), FIFO Programme and Bound solution (T2:8.2). NP-Complete
and NP-Hard problems: Basic concepts, non-deterministic algorithms, P, NP,
NP-Complete, and NP-Hard classes (T2:11.1).

RBT: L1, L2, L3

Course Outcomes: The student will be able to :
• Describe computational solution to well known problems like searching, sorting etc.
• Estimate the computational complexity of different algorithms.
• Devise an algorithm using appropriate design strategies for problem solving.
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Introduction to the Design and Analysis of Algorithms, Anany Levitin:, 2rd Edition,
2009. Pearson.
2. Computer Algorithms/C++, Ellis Horowitz, Satraj Sahni and Rajasekaran, 2nd
Edition, 2014, Universities Press
Reference Books:
1. Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronal L.
Rivest, Clifford Stein, 3rd Edition, PHI.
2. Design and Analysis of Algorithms , S. Sridhar, Oxford (Higher Education).

Artificial Intelligence and Machine Learning

OPERATING SYSTEMS
(Effective from the academic year 2018 -2019)
SEMESTER – IV
Course Code 18CS43 CIE Marks 40
Number of Contact Hours/Week 3:0:0 SEE Marks 60
Total Number of Contact Hours 40 Exam Hours 03
CREDITS –3
Course Learning Objectives: This course (18CS43) will enable students to:
• Introduce concepts and terminology used in OS
• Explain threading and multithreaded systems
• Illustrate process synchronization and concept of Deadlock
• Introduce Memory and Virtual memory management, File system and storage
techniques
Module 1 Contact
Hours
Introduction to operating systems, System structures: What operating systems 08
do; Computer System organization; Computer System architecture; Operating
System structure; Operating System operations; Process management; Memory
management; Storage management; Protection and Security; Distributed system;
Special-purpose systems; Computing environments. Operating System Services;
User - Operating System interface; System calls; Types of system calls; System
programs; Operating system design and implementation; Operating System
structure; Virtual machines; Operating System generation; System boot. Process
Management Process concept; Process scheduling; Operations on processes;
Inter process communication
Text book 1: Chapter 1, 2.1, 2.3, 2.4, 2.5, 2.6, 2.8, 2.9, 2.10, 3.1, 3.2, 3.3, 3.4
RBT: L1, L2, L3
Module 2
Multi-threaded Programming: Overview; Multithreading models; Thread 08
Libraries; Threading issues. Process Scheduling: Basic concepts; Scheduling
Criteria; Scheduling Algorithms; Multiple-processor scheduling; Thread
scheduling. Process Synchronization: Synchronization: The critical section
problem; Peterson’s solution; Synchronization hardware; Semaphores; Classical
problems of synchronization; Monitors.
Text book 1: Chapter 4.1, 4.2, 4.3, 4.4, 5.1, 5.2, 5.3, 5.4, 5.5, 6.2, 6.3, 6.4, 6.5,
6.6, 6.7
RBT: L1, L2, L3
Module 3
Deadlocks : Deadlocks; System model; Deadlock characterization; Methods for 08
handling deadlocks; Deadlock prevention; Deadlock avoidance; Deadlock
detection and recovery from deadlock. Memory Management: Memory
management strategies: Background; Swapping; Contiguous memory allocation;
Paging; Structure of page table; Segmentation.
Text book 1: Chapter 7, 8.1 to 8.6
RBT: L1, L2, L3
Module 4
Virtual Memory Management: Background; Demand paging; Copy-on-write; 08
Page replacement; Allocation of frames; Thrashing. File System,
Implementation of File System: File system: File concept; Access methods;
Directory structure; File system mounting; File sharing; Protection:
Implementing File system: File system structure; File system implementation;
Artificial Intelligence and Machine Learning
37

Directory implementation; Allocation methods; Free space management.

Text book 1: Chapter 91. To 9.6, 10.1 to 10.5

RBT: L1, L2, L3

Module 5
Secondary Storage Structures, Protection: Mass storage structures; Disk 08
structure; Disk attachment; Disk scheduling; Disk management; Swap space
management. Protection: Goals of protection, Principles of protection, Domain of
protection, Access matrix, Implementation of access matrix, Access control,
Revocation of access rights, Capability- Based systems. Case Study: The Linux
Operating System: Linux history; Design principles; Kernel modules; Process
management; Scheduling; Memory Management; File systems, Input and output;
Inter-process communication.
Text book 1: Chapter 12.1 to 12.6, 21.1 to 21.9
RBT: L1, L2, L3
Course Outcomes: The student will be able to :
• Demonstrate need for OS and different types of OS
• Apply suitable techniques for management of different resources
• Use processor, memory, storage and file system commands
• Realize the different concepts of OS in platform of usage through case studies
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Abraham Silberschatz, Peter Baer Galvin, Greg Gagne, Operating System Principles
7th edition, Wiley-India, 2006
Reference Books:
1. Ann McHoes Ida M Fylnn, Understanding Operating System, Cengage Learning, 6th
Edition
2. D.M Dhamdhere, Operating Systems: A Concept Based Approach 3rd Ed, McGraw-
Hill, 2013.
3. P.C.P. Bhatt, An Introduction to Operating Systems: Concepts and Practice 4th
Edition, PHI(EEE), 2014.
4. William Stallings Operating Systems: Internals and Design Principles, 6th Edition,
Pearson.

Artificial Intelligence and Machine Learning

MICROCONTROLLER AND EMBEDDED SYSTEMS

(Effective from the academic year 2018 -2019)
SEMESTER – IV
Course Code 18CS44 CIE Marks 40
Number of Contact Hours/Week 3:0:0 SEE Marks 60
Total Number of Contact Hours 40 Exam Hours 03
CREDITS –3
Course Learning Objectives: This course (18CS44) will enable students to:
• Understand the fundamentals of ARM based systems, basic hardware components,
selection methods and attributes of an embedded system.
• Program ARM controller using the various instructions
• Identify the applicability of the embedded system
• Comprehend the real time operating system used for the embedded system
Module 1 Contact
Hours
Microprocessors versus Microcontrollers, ARM Embedded Systems: The RISC 08
design philosophy, The ARM Design Philosophy, Embedded System Hardware,
Embedded System Software.
ARM Processor Fundamentals: Registers, Current Program Status Register,
Pipeline, Exceptions, Interrupts, and the Vector Table , Core Extensions

Text book 1: Chapter 1 - 1.1 to 1.4, Chapter 2 - 2.1 to 2.5

RBT: L1, L2
Module 2
Introduction to the ARM Instruction Set : Data Processing Instructions , 08
Programme Instructions, Software Interrupt Instructions, Program Status Register
Instructions, Coprocessor Instructions, Loading Constants

ARM programming using Assembly language: Writing Assembly code,

Profiling and cycle counting, instruction scheduling, Register Allocation,
Conditional Execution, Looping Constructs

Text book 1: Chapter 3:Sections 3.1 to 3.6 ( Excluding 3.5.2), Chapter

6(Sections 6.1 to 6.6)
RBT: L1, L2
Module 3
Embedded System Components: Embedded Vs General computing system, 08
History of embedded systems, Classification of Embedded systems, Major
applications areas of embedded systems, purpose of embedded systems

Core of an Embedded System including all types of processor/controller,

Memory, Sensors, Actuators, LED, 7 segment LED display, stepper motor,
Keyboard, Push button switch, Communication Interface (onboard and external
types), Embedded firmware, Other system components.

Text book 2:Chapter 1(Sections 1.2 to 1.6),Chapter 2(Sections 2.1 to 2.6)

RBT: L1, L2
Module 4
Embedded System Design Concepts: Characteristics and Quality Attributes of 08
Embedded Systems, Operational quality attributes ,non-operational quality
Artificial Intelligence and Machine Learning
39

attributes, Embedded Systems-Application and Domain specific, Hardware

Software Co-Design and Program Modelling, embedded firmware design and
development

Text book 2: Chapter-3, Chapter-4, Chapter-7 (Sections 7.1, 7.2 only),

Chapter-9 (Sections 9.1, 9.2, 9.3.1, 9.3.2 only)

RBT: L1, L2
Module 5
RTOS and IDE for Embedded System Design: Operating System basics, Types 08
of operating systems, Task, process and threads (Only POSIX Threads with an
example program), Thread preemption, Multiprocessing and Multitasking, Task
Communication (without any program), Task synchronization issues – Racing and
Deadlock, Concept of Binary and counting semaphores (Mutex example without
any program), How to choose an RTOS, Integration and testing of Embedded
hardware and firmware, Embedded system Development Environment – Block
diagram (excluding Keil), Disassembler/decompiler, simulator, emulator and
debugging techniques, target hardware debugging, boundary scan.

Text book 2: Chapter-10 (Sections 10.1, 10.2, 10.3, 10.4 , 10.7, 10.8.1.1,
10.8.1.2, 10.8.2.2, 10.10 only), Chapter 12, Chapter-13 ( block diagram before
13.1, 13.3, 13.4, 13.5, 13.6 only)
RBT: L1, L2
Course Outcomes: The student will be able to :
● Describe the architectural features and instructions of ARM microcontroller
● Apply the knowledge gained for Programming ARM for different applications.
● Interface external devices and I/O with ARM microcontroller.
● Interpret the basic hardware components and their selection method based on the
characteristics and attributes of an embedded system.
● Develop the hardware /software co-design and firmware design approaches.
• Demonstrate the need of real time operating system for embedded system applications
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Andrew N Sloss, Dominic Symes and Chris Wright, ARM system developers guide,
Elsevier, Morgan Kaufman publishers, 2008.
2. Shibu K V, “Introduction to Embedded Systems”, Tata McGraw Hill Education,
Private Limited, 2nd Edition.
Reference Books:
1. Raghunandan..G.H, Microcontroller (ARM) and Embedded System, Cengage
learning Publication,2019
2. The Insider’s Guide to the ARM7 Based Microcontrollers, Hitex Ltd.,1st edition,
2005.

Artificial Intelligence and Machine Learning

3. Steve Furber, ARM System-on-Chip Architecture, Second Edition, Pearson, 2015.

4. Raj Kamal, Embedded System, Tata McGraw-Hill Publishers, 2nd Edition, 2008.

Artificial Intelligence and Machine Learning

OBJECT ORIENTED CONCEPTS

(Effective from the academic year 2018 -2019)
SEMESTER – IV
Course Code 18CS45 CIE Marks 40
Number of Contact Hours/Week 3:0:0 SEE Marks 60
Total Number of Contact Hours 40 Exam Hours 03
CREDITS –3
Course Learning Objectives: This course (18CS45) will enable students to:
• Learn fundamental features of object oriented language and JAVA
• Set up Java JDK environment to create, debug and run simple Java programs.
• Create multi-threaded programs and event handling mechanisms.
• Introduce event driven Graphical User Interface (GUI) programming using applets
and swings.
Module 1 Contact
Hours
Introduction to Object Oriented Concepts: 08
A Review of structures, Procedure–Oriented Programming system, Object
Oriented Programming System, Comparison of Object Oriented Language with
C, Console I/O, variables and reference variables, Function Prototyping, Function
Overloading. Class and Objects: Introduction, member functions and data,
objects and functions.
Text book 1: Ch 1: 1.1 to 1.9 Ch 2: 2.1 to 2.3
RBT: L1, L2
Module 2
Class and Objects (contd): 08
Objects and arrays, Namespaces, Nested classes, Constructors, Destructors.
Introduction to Java: Java’s magic: the Byte code; Java Development Kit (JDK);
the Java Buzzwords, Object-oriented programming; Simple Java programs. Data
types, variables and arrays, Operators, Control Statements.
Text book 1:Ch 2: 2.4 to 2.6Ch 4: 4.1 to 4.2
Text book 2: Ch:1 Ch: 2 Ch:3 Ch:4 Ch:5
RBT: L1, L2
Module 3
Classes, Inheritance,Exception Handling: Classes: Classes fundamentals; 08
Declaring objects; Constructors, this keyword, garbage collection. Inheritance:
inheritance basics, using super, creating multi level hierarchy, method overriding.
Exception handling: Exception handling in Java.
Text book 2: Ch:6 Ch: 8 Ch:10
RBT: L1, L2, L3
Module 4
Packages and Interfaces:Packages, Access Protection,Importing 08
Packages.Interfaces.
Multi Threaded Programming:Multi Threaded Programming: What are
threads? How to make the classes threadable ; Extending threads; Implementing
runnable; Synchronization; Changing state of the thread; Bounded buffer
problems, producer consumer problems.
Text book 2: CH: 9 Ch 11:
RBT: L1, L2, L3
Artificial Intelligence and Machine Learning
42

Module 5
Event Handling: Two event handling mechanisms; The delegation event model; 08
Event classes; Sources of events; Event listener interfaces; Using the delegation
event model; Adapter classes; Inner classes.
Swings: Swings: The origins of Swing; Two key Swing features; Components
and Containers; The Swing Packages; A simple Swing Application; Create a
Swing Applet; Jlabel and ImageIcon; JTextField;The Swing Buttons;
JTabbedpane; JScrollPane; JList; JComboBox; JTable.
Text book 2: Ch 22: Ch: 29 Ch: 30
RBT: L1, L2, L3
Course Outcomes: The student will be able to :
• Explain the object-oriented concepts and JAVA.
• Develop computer programs to solve real world problems in Java.
• Develop simple GUI interfaces for a computer program to interact with users, and to
understand the event-based GUI handling principles using swings.
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Sourav Sahay, Object Oriented Programming with C++ , 2nd Ed, Oxford University
Press,2006
2. Herbert Schildt, Java The Complete Reference, 7th Edition, Tata McGraw Hill, 2007.

Reference Books:
1. Mahesh Bhave and Sunil Patekar, "Programming with Java", First Edition, Pearson
Education,2008, ISBN:9788131720806
2. Herbert Schildt, The Complete Reference C++, 4th Edition, Tata McGraw Hill,
2003.
3. Stanley B.Lippmann, Josee Lajore, C++ Primer, 4th Edition, Pearson Education,
2005.
4. Rajkumar Buyya,S Thamarasi selvi, xingchen chu, Object oriented Programming with
java, Tata McGraw Hill education private limited.
5. Richard A Johnson, Introduction to Java Programming and OOAD, CENGAGE
Learning.
6. E Balagurusamy, Programming with Java A primer, Tata McGraw Hill companies.
Mandatory Note: Every institute shall organize bridge course on C++, either in the
vacation or in the beginning of even semester for a minimum period of ten days
(2hrs/day). Maintain a copy of the report for verification during LIC visit.
Faculty can utilize open source tools to make teaching and learning more interactive.

Artificial Intelligence and Machine Learning

DATA COMMUNICATION
(Effective from the academic year 2018 -2019)
SEMESTER – IV
Course Code 18CS46 CIE Marks 40
Number of Contact Hours/Week 3:0:0 SEE Marks 60
Total Number of Contact Hours 40 Exam Hours 03
CREDITS –3
Course Learning Objectives: This course (18CS46) will enable students to:
• Comprehend the transmission technique of digital data between two or more
computers and a computer network that allows computers to exchange data.
• Explain with the basics of data communication and various types of computer
networks;
• Demonstrate Medium Access Control protocols for reliable and noisy channels.
• Expose wireless and wired LANs.
Module 1 Contact
Hours
Introduction: Data Communications, Networks, Network Types, Internet 08
History, Standards and Administration, Networks Models: Protocol Layering,
TCP/IP Protocol suite, The OSI model, Introduction to Physical Layer-1: Data
and Signals, Digital Signals, Transmission Impairment, Data Rate limits,
Performance.
Textbook1: Ch 1.1 to 1.5, 2.1 to 2.3, 3.1, 3.3 to 3.6

RBT: L1, L2

Module 2
Digital Transmission: Digital to digital conversion (Only Line coding: Polar, 08
Bipolar and Manchester coding).
Physical Layer-2: Analog to digital conversion (only PCM), Transmission
Modes,
Analog Transmission: Digital to analog conversion.
Textbook1: Ch 4.1 to 4.3, 5.1
RBT: L1, L2
Module 3
Bandwidth Utilization: Multiplexing and Spread Spectrum, 08
Switching: Introduction, Circuit Switched Networks and Packet switching.
Error Detection and Correction: Introduction, Block coding, Cyclic codes,
Checksum,
Textbook1: Ch 6.1, 6.2, 8.1 to 8.3, 10.1 to 10.4
RBT: L1, L2
Module 4
Data link control: DLC services, Data link layer protocols, Point to Point 08
protocol (Framing, Transition phases only).
Media Access control: Random Access, Controlled Access and Channelization,
Introduction to Data-Link Layer: Introduction, Link-Layer Addressing, ARP
IPv4 Addressing and subnetting: Classful and CIDR addressing, DHCP, NAT
Textbook1: Ch 9.1, 9.2, 11.1, 11.2 11.4, 12.1 to 12.3, 18.4
RBT: L1, L2
Module 5
Artificial Intelligence and Machine Learning
44

Wired LANs Ethernet: Ethernet Protocol, Standard Ethernet, Fast Ethernet, 08

Gigabit Ethernet and 10 Gigabit Ethernet,
Wireless LANs: Introduction, IEEE 802.11 Project and Bluetooth.
Other wireless Networks: Cellular Telephony
Textbook1: Ch 13.1 to 13.5, 15.1 to 15.3, 16.2
RBT: L1, L2

Course Outcomes: The student will be able to :

• Explain the various components of data communication.
• Explain the fundamentals of digital communication and switching.
• Compare and contrast data link layer protocols.
• Summarize IEEE 802.xx standards
Question Paper Pattern:
• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each
module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from
each module.
Textbooks:
1. Behrouz A. Forouzan, Data Communications and Networking 5E, 5th Edition, Tata
McGraw-Hill, 2013.
Reference Books:
1. Alberto Leon-Garcia and Indra Widjaja: Communication Networks - Fundamental
Concepts and Key architectures, 2nd Edition Tata McGraw-Hill, 2004.
2. William Stallings: Data and Computer Communication, 8th Edition, Pearson
Education, 2007.
3. Larry L. Peterson and Bruce S. Davie: Computer Networks – A Systems Approach,
4th Edition, Elsevier, 2007.
4. Nader F. Mir: Computer and Communication Networks, Pearson Education, 2007.

Artificial Intelligence and Machine Learning

DESIGN AND ANALYSIS OF ALGORITHMS LABORATORY

(Effective from the academic year 2018 -2019)
SEMESTER – IV
Course Code 18CSL47 CIE Marks 40
Number of Contact Hours/Week 0:2:2 SEE Marks 60
Total Number of Lab Contact Hours 36 Exam Hours 03
Credits – 2
Course Learning Objectives: This course (18CSL47) will enable students to:
• Design and implement various algorithms in JAVA
• Employ various design strategies for problem solving.
• Measure and compare the performance of different algorithms.
Descriptions (if any):
• Design, develop, and implement the specified algorithms for the following problems
using Java language under LINUX /Windows environment. Netbeans / Eclipse or
IntellijIdea Community Edition IDE tool can be used for development and
demonstration.
• Installation procedure of the required software must be demonstrated, carried
out in groups and documented in the journal.
Programs List:
1.
a. Create a Java class called Student with the following details as variables within it.
(i) USN
(ii) Name
(iii) Programme
(iv) Phone
Write a Java program to create nStudent objects and print the USN, Name, Programme,
and Phoneof these objects with suitable headings.
b. Write a Java program to implement the Stack using arrays. Write Push(), Pop(), and
Display() methods to demonstrate its working.
2.
a. Design a superclass called Staff with details as StaffId, Name, Phone, Salary. Extend
this class by writing three subclasses namely Teaching (domain, publications),
Technical (skills), and Contract (period). Write a Java program to read and display at
least 3 staff objects of all three categories.
b. Write a Java class called Customer to store their name and date_of_birth. The
date_of_birth format should be dd/mm/yyyy. Write methods to read customer data as
<name, dd/mm/yyyy> and display as <name, dd, mm, yyyy> using StringTokenizer
class considering the delimiter character as “/”.
3.
a. Write a Java program to read two integers a andb. Compute a/b and print, when b
is not zero. Raise an exception when b is equal to zero.
b. Write a Java program that implements a multi-thread application that has three threads.
First thread generates a random integer for every 1 second; second thread computes the
square of the number andprints; third thread will print the value of cube of the number.
4. Sort a given set of n integer elements using Quick Sort method and compute its time
complexity. Run the program for varied values of n> 5000 and record the time taken to
sort. Plot a graph of the time taken versus non graph sheet. The elements can be read
from a file or can be generated using the random number generator. Demonstrate using
Java how the divide-and-conquer method works along with its time complexity analysis:
worst case, average case and best case.
5. Sort a given set of n integer elements using Merge Sort method and compute its time
complexity. Run the program for varied values of n> 5000, and record the time taken to
sort. Plot a graph of the time taken versus non graph sheet. The elements can be read
Artificial Intelligence and Machine Learning
46

from a file or can be generated using the random number generator. Demonstrate using
Java how the divide-and-conquer method works along with its time complexity analysis:
worst case, average case and best case.
6. Implement in Java, the 0/1 Knapsack problem using (a) Dynamic Programming method
(b) Greedy method.
7. From a given vertex in a weighted connected graph, find shortest paths to other vertices
using Dijkstra's algorithm. Write the program in Java.
8. Find Minimum Cost Spanning Tree of a given connected undirected graph using
Kruskal'salgorithm. Use Union-Find algorithms in your program
9. Find Minimum Cost Spanning Tree of a given connected undirected graph using
Prim's algorithm.
10. Write Java programs to
(a) Implement All-Pairs Shortest Paths problem using Floyd's algorithm.
(b) Implement Travelling Sales Person problem using Dynamic programming.
11. Design and implement in Java to find a subset of a given set S = {Sl, S2,.....,Sn} of n
positive integers whose SUM is equal to a given positive integer d. For example, if S
={1, 2, 5, 6, 8} and d= 9, there are two solutions {1,2,6}and {1,8}. Display a suitable
message, if the given problem instance doesn't have a solution.
12. Design and implement in Java to find all Hamiltonian Cycles in a connected
undirected Graph G of n vertices using backtracking principle.
Laboratory Outcomes: The student should be able to:
• Design algorithms using appropriate design techniques (brute-force, greedy, dynamic
programming, etc.)
• Implement a variety of algorithms such assorting, graph related, combinatorial, etc., in
a high level language.
• Analyze and compare the performance of algorithms using language features.
• Apply and implement learned algorithm design techniques and data structuresto solve
real-world problems.
Conduct of Practical Examination:
• Experiment distribution
o For laboratories having only one part: Students are allowed to pick one
experiment from the lot with equal opportunity.
o For laboratories having PART A and PART B: Students are allowed to pick
one experiment from PART A and one experiment from PART B, with equal
opportunity.
• Change of experiment is allowed only once and marks allotted for procedure to be
made zero of the changed part only.
• Marks Distribution (Courseed to change in accoradance with university regulations)
e) For laboratories having only one part – Procedure + Execution + Viva-Voce:
15+70+15 = 100 Marks
f) For laboratories having PART A and PART B
i. Part A – Procedure + Execution + Viva = 6 + 28 + 6 = 40 Marks
ii. Part B – Procedure + Execution + Viva = 9 + 42 + 9 = 60 Marks

Artificial Intelligence and Machine Learning

MICROCONTROLLER AND EMBEDDED SYSTEMS LABORATORY

(Effective from the academic year 2018 -2019)
SEMESTER – IV
Course Code 18CSL48 CIE Marks 40
Number of Contact Hours/Week 0:2:2 SEE Marks 60
Total Number of Lab Contact Hours 36 Exam Hours 03
Credits – 2
Course Learning Objectives: This course (18CSL48) will enable students to:
• Develop and test Program using ARM7TDMI/LPC2148
• Conduct the experiments on an ARM7TDMI/LPC2148 evaluation board using evaluation
version of Embedded 'C' & Keil Uvision-4 tool/compiler.
Descriptions (if any):

Programs List:
PART A Conduct the following experiments by writing program using ARM7TDMI/LPC2148
using an evaluation board/simulator and the required software tool.
1. Write a program to multiply two 16 bit binary numbers.
2. Write a program to find the sum of first 10 integer numbers.
3. Write a program to find factorial of a number.
4. Write a program to add an array of 16 bit numbers and store the 32 bit result in internal
RAM
5. Write a program to find the square of a number (1 to 10) using look-up table.
6. Write a program to find the largest/smallest number in an array of 32 numbers .
7. Write a program to arrange a series of 32 bit numbers in ascending/descending order.
8. Write a program to count the number of ones and zeros in two consecutive memory
locations.
PART –B Conduct the following experiments on an ARM7TDMI/LPC2148 evaluation board
using evaluation version of Embedded 'C' & Keil Uvision-4 tool/compiler.
9. Display “Hello World” message using Internal UART.
10. Interface and Control a DC Motor.
11. Interface a Stepper motor and rotate it in clockwise and anti-clockwise direction.
12. Determine Digital output for a given Analog input using Internal ADC of ARM
controller.
13. Interface a DAC and generate Triangular and Square waveforms.
14. Interface a 4x4 keyboard and display the key code on an LCD.
15. Demonstrate the use of an external interrupt to toggle an LED On/Off.
16. Display the Hex digits 0 to F on a 7-segment LED interface, with an appropriate delay in
between

Laboratory Outcomes: The student should be able to:

• Develop and test program using ARM7TDMI/LPC2148
• Conduct the following experiments on an ARM7TDMI/LPC2148 evaluation board using
evaluation version of Embedded 'C' & Keil Uvision-4 tool/compiler.
Conduct of Practical Examination:
• Experiment distribution
o For laboratories having only one part: Students are allowed to pick one experiment
from the lot with equal opportunity.
o For laboratories having PART A and PART B: Students are allowed to pick one
experiment from PART A and one experiment from PART B, with equal
opportunity.
Artificial Intelligence and Machine Learning
48

• Change of experiment is allowed only once and marks allotted for procedure to be made
zero of the changed part only.
• Marks Distribution (Courseed to change in accoradance with university regulations)
g) For laboratories having only one part – Procedure + Execution + Viva-Voce:
15+70+15 = 100 Marks
h) For laboratories having PART A and PART B
i. Part A – Procedure + Execution + Viva = 6 + 28 + 6 = 40 Marks
ii. Part B – Procedure + Execution + Viva = 9 + 42 + 9 = 60 Marks

Artificial Intelligence and Machine Learning

B. E. Common to all Programmes

Outcome Based Education (OBE) and Choice Based Credit System (CBCS)
SEMESTER - IV
ADDITIONAL MATHEMATICS – II
(Mandatory Learning Course: Common to All Programmes)
(A Bridge course for Lateral Entry students under Diploma quota to BE/B. Tech. programmes)
Course Code 18MATDIP41 CIE Marks 40
Teaching Hours/Week (L:T:P) (2:1:0) SEE Marks 60
Credits 0 Exam Hours 03
Course Learning Objectives:
• To provide essential concepts of linear algebra, second & higher order differential equations
along with methods to solve them.
• To provide an insight into elementary probability theory and numerical methods.
Module-1
Linear Algebra: Introduction - rank of matrix by elementary row operations - Echelon form.
Consistency of system of linear equations - Gauss elimination method. Eigen values and Eigen vectors
of a square matrix. Problems.
Module-2
Numerical Methods: Finite differences. Interpolation/extrapolation using Newton’s forward and
backward difference formulae (Statements only)-problems. Solution of polynomial and
transcendental equations – Newton-Raphson and Regula-Falsi methods (only formulae)- Illustrative
examples. Numerical integration: Simpson’s one third rule and Weddle’s rule (without proof)
P bl
Module-3
Higher order ODE’s: Linear differential equations of second and higher order equations with
constant coefficients. Homogeneous /non-homogeneous equations. Inverse differential
operators.[Particular Integral restricted to R(x)= e ax , sin ax /cos ax for f (D ) y = R(x ). ]
Module-4
Partial Differential Equations (PDE’s):- Formation of PDE’s by elimination of arbitrary constants
and functions. Solution of non-homogeneous PDE by direct integration. Homogeneous PDEs
involving derivative with respect to one independent variable only.
Module-5
Probability: Introduction. Sample space and events. Axioms of probability. Addition &
multiplication theorems. Conditional probability, Bayes’s theorem, problems.

Course Outcomes: At the end of the course the student will be able to:
CO1: Solve systems of linear equations using matrix algebra.
CO2: Apply the knowledge of numerical methods in modelling and solving engineering problems.
CO3: Make use of analytical methods to solve higher order differential equations.
CO4: Classify partial differential equations and solve them by exact methods.
CO5: Apply elementary probability theory and solve related problems.
Question paper pattern:
• The question paper will have ten full questions carrying equal marks.
• Each full question will be for 20 marks.
• There will be two full questions (with a maximum of four sub- questions) from each module.
• Each full question will have sub- question covering all the topics under a module.
• The students will have to answer five full questions, selecting one full question from each
d l
Sl Name of the Edition and
Title of the Book Name of the Publisher
No Author/s Year
Artificial Intelligence and Machine Learning
50

Textbook
1 Higher Engineering B.S. Grewal Khanna Publishers 43rd Edition,
Mathematics 2015
Reference Books
1 Advanced Engineering E. Kreyszig John Wiley & Sons 10th Edition,
Mathematics 2015
2 Engineering Mathematics N. P. Bali and Laxmi Publishers 7th Edition, 2007
Manish Goyal
3 Engineering Mathematics Vol. Rohit Khurana Cengage Learning 1st Edition, 2015
I

Artificial Intelligence and Machine Learning

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