Handbook of B. Tech. Progrannes offered by USICT at Afiliated Institutions of the University.

Principles of Entrepreneurship Mindset L P C

2 2

Discipline(s) /EAE / OAE Semester Group Sub-group Paper Code

All 7 HS/MS MS MS-401

Marking Scheme:
1 Teachers Continuous Evaluation: 25 marks
2. Term end Theory Examinations: 75 marks
Instructions for paper setter:
1. There should be 9 questions in the term end examinations question paper.
2. The first (1st) question should be compulsory and cover the entire syllabus. This question should be
objective, single line answers or short answer type question of total 15 marks.
3. Apart from question 1 which is compulsory, rest of the paper shall consist of 4 units as per the syllabus.
Every unit shall have two questions covering the corresponding unit of the syllabus. However, thee student
shall be asked to attempt only one of the two questions in the unit. Individual questions may contain upto
5 sub-parts / sub-questions. Each Unit shall have a marks weightage of 15.
4 The questions are to be framed keeping in view the learning outcomes of the course/ paper. The standard
/level of thequestions to be asked should be at the level of the prescribed textbook.
5. The requirement of (scientific) calculators / log-tables / data -tables may be specified if required.
Course Objectives:
1. To understand basic aspects of establishing a business in a competitive environment
2. To apply the basic understanding to examine the existing business ventures
To examine various business considerations such as marketing, financial and teaming etc.
4. To assess strategies for planning a business venture
Course Outcomes (CO)
Co 1 Understand basic aspects of establishing a business in a competitive environment
C0 2 Apply the basic understanding to examine the existing business ventures
CO 3 Examine various business considerations such as marketing, financial and teaming etc.
CO 4 Assessing strategies for planning a business venture
Course Outcomes (CO) to Programme Outcomes (PO) mapping (scale 1: low, 2: Medium, 3: High)
PO01 PO02 PO03 PO04 POO5 POO6 PO07 PO08 PO09 PO10 PO11 PO12
CO 1 2 2 1 2 2 1 2 3 2
CO 2 2 2 1 2 2 1 2 2
CO 3 2 1 2 2 1 2 2
CO 4 2 2 1 2 2 1 2 3 2

UNIT-I

Entrepreneurial perspective: Foundation, Nature and development ofentrepreneurship, importance of

entrepreneurs, Entrepreneurial Mind, Individual entrepreneur Typesof entrepreneurs, Entrepreneurship in
India

UNIT-I|

Beginning Considerations: Creativity and developing business ideas; Creatingand starting the venture;
Building a competitiveadvantage; Opportunity recognition, Opportunityassessment; Legal issues
UNIT-I||

Developing Financial Plans: Sources of Funds, Managing Cash Flow,Creating a successful Financial
Plan Developing a business plan

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Handbook of B.Tech. Programmes offered by USICT at Affiliated Institutions of the University.

UNIT -IV

Developing Marketing Plans: Developing a powerful Marketing Plan, E-commerce,Integrated Marketing

Communications
Leading Considerations: Developing Team, Inviting candidates to join team,Leadership model

Textbook(s):
1. Robert DHisrich, Michael P Peters &Dean AShepherd, "Entrepreneurship" 10th Edition,McGraw Hill
Education, 2018

References:
1. Norman M. Scarborough and Jeffery R. cornwelI, "Essentials of entrepreneurship and small business
management" 8th Edition, Pearson, 2016
2. Rajiv Roy, "Entrepreneurship", 2nd Edition, Oxford University Press, 2011
3. Sangeeta Sharma, "Entrepreneurship Development", 1st Edition, Prentice-Hall India, 2016
4. John Mullins, The New Business Road Test: What entrepreneurs and investors should dobefore launching
a lean start-up" 5th Edition, Pearson Education, 2017
5. Charantimath, Entrepreneurship Development and Small Business Enterprise, Pearson Education.
 Next Generation Web PC
3 3

Discipline(s) /EAE / OAE Semester Group Sub-group Paper Code

CSE 7 PCE PCE-4 CIE-413T

Marking Scheme:
1. Teachers Continuous Evaluation: 25 marks
2. Term end Theory Examinations: 75 marks
Instructions for paper setter:
1. There should be 9 questions in the term end examinations question paper.
2. The first (1st) question should be compulsory and cover the entire syllabus. This question should be
objective, single line answers or short answer type question of total 15 marks.
3. Apart from question 1 which is compulsory, rest of the paper shall consist of 4 units as per the syllabus.
Every unit shall have two questions covering the corresponding unit of the syllabus. However, the student
shall be asked to attempt only one of the two questions in the unit. Individual questions may contain upto
5 sub-parts/ sub-questions. Each Unit shall have a marks weightage of 15.
4. The questions are to be framed keeping in view the learning outcomes of the course/ paper. The standard
/ level of the questions to be asked should be at the level of the prescribed textbook.
5. The requirement of (scientific) calculators / log-tables/ data -tables may be specified if required.
Course Objectives :
1. To introduce the basic con cept associated with Internet and internet protocols
2. To understand the Database Connectivity.
3. To understand the Web Page, Website and Web Application
4. To describe the various Web attacks and their preventions
Course Outcomes (CO)
CO 1 To understand the basic concepts of Internet and World Wide Web
CO 2 To develop the concept of Web Technologies
CO 3 Understand the functionalities of Web Engineering Technologies in distributed systems.
CO 4 ldentifying the issues in Security Threats and Security risks of asite.
Course Outcomes (CO) to Programme Outcomes (PO) mapping (scale 1: low, 2: Medium, 3: High)
POo1 PO02 PO03 PO04 POO5 PO06 PO07 PO08 PO09 PO10 PO11 PO12
CO 1 3 3 3 -
3
CO 2 3 2 2 -
3 3
CO 3 3 3 2 3 3 3
CO 4 2 3 3 3

UNIT-I

Growth of Internet, Basic internet protocols, History of the Internet, World Wide Web, HTTP: Hypertext
Transfer Protocol, Markup languages-XHTML, Introduction to HTML, Basics of XTHML, DHTML, and XML
Anatomy of Internet, APRANET and Internet history of the World Web, Basic Internet Terminology, Internet
Protocols: TCP/IP, Router, Internet Addressing Scheme, Machine Addressing (IP address), E-mail Address, XML
versions & declarations, Introduction to WML.

UNIT-II

Database Connectivity: JDBC, ODBC, Database-to web connectivity, Web Page, Website and Web Application,
Technology Framework for development, Client-side scripting: JavaScript, Client Side Programming: JAVA
Scripts, basic syntax, variables & data-types, literals, functions, objects, arrays and built-in objects, Server side
programming, Java Servlets, Life cycle, parameter data, sessions, cookies, servlets capabilities, servlets &
concurrency.

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Handbook of B. Tech. Programmes offered by USICT at Affiliated Instiutions of the University.

UNIT-III

Web attacks and their prevention, Security Threats, Security risks of a site, Session management,
authentication, HTTPS and certificates, Firewalls (WAFS), Web security model, Client-side security, Cookies
security policy, HTTP security extensions, Web user tracking, Server-side security tools, Web Application and
Fuzzers.

UNIT -IV

Concept and issues of Web 2.0 and Web 3.0, Latest Trends in Web Technologies, Search Engines, Web
rawling, Search Engine Optimization, Web Security concerns, Applications of Web Engineering Technologies
in distributed systems etc, Case studies using different tools, Web IR System, Web Analytics, Web Mining
Framework, Social Web Mining and Text Mining.

Textbook(s):
1 Internet and Web Technologies by Raj Kamal, Tata McGraw Hill edition. (ISBN: 9780070472969), 2002
Web Technologies: A Computer Science Perspective, Jackson, Pearson Education India, 2007.
3. Modeling the Internet and the Web, Pierre Baldi, Paolo Frasconi, Padhraic Smyth, John Wiley and Sons
Ltd.

References:
1. Achyut Godbole, Atul Kahate, "Web Technologies", MCGraw-Hill Education, Third Edition.
2. PHP and MySQL for Dynamic Web Sites, Ullman, Larry, Peachpit Press.1 (1SBN: 978-0-321-78407o), 2012.
3. Chris Bates, "Web Programming", Wiley
 Handbook of B.Tech. Programmes offered by USICT at Afiliated Institutions of the University.

Web Mining C
3 3

Discipline(s) /EAE/OAE Semester Group Sub-group Paper Code

CSE/IT 7 PCE PCE-5 CIE-431T

Marking Scheme:
1. Teachers Continuous Evaluation: 25 marks
2. Term end Theory Examinations: 75 marks
Instructions for paper setter:
1. There should be 9 questions in the term end examinations question paper.
2. The first (1st) question should be compulsory and cover the entire syllabus. This question should be
objective, single line answers or short answer type question of total 15 marks.
3. Apart from question 1 which is compulsory, rest of the paper shall consist of 4 units as per the syllabus.
Every unit shall have two questions covering the corresponding unit of the syllabus. However, the student
shall be asked to attempt only one of the two questions in the unit. Individual questions may contain upto
5 sub-parts / sub-questions. Each Unit shall have a marks weightage of 15.
4 The questions are to be framed keeping in view the learning outcomes of the course/ paper. The standard
/ level of the questions to be asked should be at the level of the prescribed textbook.
5. The requirement of (scientific) calculators / log-tables / data - tables may be specified if required.
Course Objectives :
1. To understand the scope of Web mining, identifying the opportunities and the challenges
2. To learn to apply different data mining/ML techniques in web mining
3. To learn graph based representation of WWW
4. To understand techniques for web crawling for web contents to build useful statistics like page
ranking
Course Outcomes (CO)
CO 1 Understand the scope of Web mining, identifying the opportunities and the challenges
CO 2 Learn to apply different data mining/ML techniques in web mining
CO 3 Learn graph based representation of www
CO 4 Understand techniques for web crawling for web contents to build useful statistics like page ranking
Course Outcomes (co) to Programme Outcomes (PO) mapping (scale 1: low, 2: Medium, 3: High)
PO01 PO02 PO03 PO04 POOs POO6 PO07 PO08 PO09 PO10PO11 PO12
CO 1 3 -

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

2
CO 4 3 |1
UNIT-I

World Wide Web- Data Mining Vs Web Mining - Data Mining Foundations: Association rules and Sequential
Patterns - Machine Learning in Data Mining, Web Mining: Web Structure Mining, Web Content Mining, and
Web Usage Mining. Web Structure Mining: Web Graph Extracting pattern from hyperlinks Mining
Document Structure - PageRank.

UNIT-II

Web Content Mining: Text and Web Page Pre-processing - Inverted Indices - Latent Semantic Indexing - Web
Spamming - Social Network Analysis - Web Crawlers - Structured Data Extraction - Opinion mining and
Sentiment Analysis.

UNIT-I|

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Handbook of B. Tech. Programmes offered by USICT at Afiliated Institutions of the University.

Web usage Mining: Data collection and Pre-processing - Data Modelling - Discovery and Analysis of Web
Usage- Recommender System and Collaborative Filtering -Query log mining

UNIT-IV

Web Mining Applications and Other Topics: Data integration for e-commerce, Web personalization and
recommender systems, Web content and structure mining, Web data warehousing, Review of tools,
applications, and systems

Textbook(s):
1. LiuB. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer Science &Business Media;
2007.

References:
1. Markov Z, Larose DT. Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage.
John Wiley & Sons; 2007.
2. Web Mining:: Applications and Techniques by Anthony Scime
3. Mining the Web: Discovering Knowledge from Hypertext Data by Soumen Chakrabarti
 Reinforcement Learning and Deep Learning L C
3

Discipline(s) /EAE / OAE Semester Group Sub-group Paper Code

CSE-AIML 7 PC PC ML-409T
EAE 7 AIML-EAE AIML-EAE-4 ML-409T

Marking Scheme:
1. Teachers Continuous Evaluation: 25 marks
2. Term end Theory Examinations: 75 marks
Instructions for paper setter:
1. There should be 9 questions in the term end examinations question paper.
2. The first (1st) question should be compulsory and cover the entire syllabus. This question should be
objective, single line answers or short answer type question of total 15 marks.
3. Apart from question 1 which is compulsory, rest of the paper shall consist of 4 units as per the syllabus.
Every unit shall have two questions covering the corresponding unit of the syllabus. However, the student
shall be asked to attempt only one of the two questions in the unit. Individual questions may contain upto
5 sub-parts/sub-questions. Each Unit shall have a marks weightage of 15.
4. The questions are to be framed keeping in view the learning outcomes of the course/ paper. The standard
/ level of the questions to be asked should be at the level of the prescribed textbook.
5. The requirement of (scientific) calculators/ log-tables / data -tables may be specified if required.
Course Objectives :
To introduce the foundation of Reinforcement learning foundation and Q Network algorithm)
2 To understand policy optimization ,recent advanced techniques and applications of Reinforcement
learning
3 To introduce the concept of deep learning and neural network
4 To understand the concept of NLP and computer vision in deep learning
Course Outcomes (CO)
CO 1 Learn how to define RL tasks and the core principals behind the RL, including policies, value functions,
deriving Bellman equations and underst and work with approximate solution(deep Q Network based
algorithms)
CO 2 Learn the policy gradient methods from vanilla to more complex cases and learn application and
advanced techniques in Reinforcement Learning
CO 3 Apply neural networks for problem solving
CO 4 Able to Analyse images and have basic understanding of NLP in deep learning
Course Outcomes (CO) to Programme Outcomes (PO) mapping (scale 1: low, 2: Medium, 3: High)
PO01 PO02 P003 PO04 POO5 PO06 PO07 PO08 P009 PO10 PO11 PO12
CO 1 3 2 3 3 3 2 2 -

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

UNIT-I

Reinforcement Learning Foundation: Introduction to Reinforcement learning and its terms,Features and
elements of RL, Defining RL Framework and Markov Decision Process, Polices, Value Functions and Bellman
Equations, Exploration vs. Exploitation, Code Standards and Libraries used in RL (Python/Keras/Tensorflow)
Tabular Methods and Q-networks: Planning through the use of Dynamic Programming and Monte Carlo,
Temporal-Difference learning methods (TD(0), SARSA, Q-Learning), Deep Q-networks (DQN, DDON, Dueling
DON, Prioritised Experience Replay)

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Handbook of B. Tech. Programmes offered by USICT at Afiliated Institutions of the University.

UNIT-I|

Policy Optimization: Introduction to policy-based methods, Vanilla Policy Gradient, REINFORCE algorithm and
stochastic policy search, Actor-critic methods (A2C, A3C) Advanced policy gradient (PPO, TRPO, DDPG),
Model-Based RL: Model-based RL approach
Recent Advances and Applications: Meta-learning. Multi-Agent Reinforcement Learning, Partially Observable
Markov Decision Process, Applying RL for real-world problems

UNIT-I||

Introduction to Deep learning: Introduction to deep learning and its application,Examples of deep learning
Introduction to Neural Network: Introduction to Neural Network its types and application, Introduction to
keras,Introduction to ANN Perceptron and its uses, Multilayer perceptron and deep neural network,Activation
function and its working TanH function,sigma ,relu etc,Feed forward network, Cost function, Backpropagation,
Gradient Descent, Regulariztion and dropout technique, Batch normalization.
Types of Neural Network: Convolutional Neural network,CNN Pooling,CNN Layers,Flattening and Full
connection, Preparing a fully connected neural network, Introduction to RNN, Deep RNN, Long Short Term
Memory, GRU, Transfer Learning,

UNIT- IV

Deep Learning for Natural Language Processing: Introduction to NLP and Vector Space Model of Semantics
Word Vector Representations: Continuous Skip-Gram Model, Continuous Bag-of-Words model (CBOW), Glove,
Evaluations and Applications in word similarity, analogy reasoning
Deep Learning for Computer Vision: Image segmentation, object detection, automatic image captioning,
Image generation with Generative adversarial networks, video to text with LSTM models. Attention models
for computer vision tasks.
 Handbook of B. Tech. Programmes offered by USICT at Affiliated nstitutions of the University.

Pattern Recognition and Computer Vision LP C

3 3

Discipline(s) /EAE/OAE Semester Group Sub-group Paper Code

CSE-AIML 7 PC PC ML-411T
EAE AIML-EAE AIML-EAE-5 ML-411T

Marking Scheme:
1. Teachers Continuous Evaluation: 25 marks
2. Term end Theory Examinations: 75 marks
Instructions for paper setter:
1. There should be 9 questions in the term end examinations question paper.
2. The first (1st) question should be compulsory and cover the entire syllabus. This question should be
objective, single line answers or short answer type question of total 15 marks.
3. Apart from question 1 which is compulsory, rest of the paper shall consist of 4 units as per the syllabus.
Every unit shall have two questions covering the corresponding unit of the syllabus. However, the student
shall be asked to attempt only one of the two questions in the unit. Individual questions may contain upto
5 sub-parts /sub-questions. Each Unit shall have a marks weightage of 15.
4. The questions are to be framed keeping in view the learning outcomes of the course / paper. The standard
/level of the questions to be asked should be at the level of the prescribed textbook.
5. The requirement of (scientific) calculators / log-tables /data -tables may be specified if required.
Course Objectives:
1 Understand the in-depth concept of Pattern Recognition
Implement Bayes Decision Theory
3 Understand the in-depth concept of Perception and related Concepts
4. Understand the concept of ML Pattern Classification
Course Outcomes (CO)
Co 1 Discuss various concepts of pattern recognition
CO 2 Understanding various algorithms
CO 3 Explain and apply various computer vision techniques
Co 4 Describe the concept of shape analysis and filtering
Course Outcomes (CO) to Programme Outcomes (PO) mapping (scale 1: low, 2: Medium, 3: High)
POO1 PO02 PO03 POO4 POO5 PO06 PO07 PO08 PO09 PO10 PO11 PO12
CO 1 2 3 3 2 1
CO 2 3 3 1 1 -
1 1 2 2 1
CO 3 3 2 3 3 2 2 2 3 1
CO 4 1 2 3 2 2 1 1 2

UNIT-I

Induction Algorithms. Rule Induction. Decision Trees. Bayesian Methods. The Basic Naive Bayes Classifier.
Naive Bayes Induction for Numeric Attributes. Correction to the Probability Estimation. Laplace Correction. No
Match. Other Bayesian Methods, Other Induction Methods. Neural Networks. Genetic Algorithms. Instance
based Learning. Support Vector Machines.
UNIT-I|

About Statistical Pattern Recognition. Classification and regression. Features and Feature Vectors, and
Classifiers. Pre-processing and feature extraction. The curse of dimensionality. Polynomial curve fitting. Model
complexity. Multivariate non-linear functions. Bayes' theorem. Decision boundaries. Parametric methods.
Sequential parameter estimation. Linear discriminant functions. Fisher's linear discriminant. Feed-forward
network mappings.

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Handbook of B. Tech. Programmes offered by USICTat Affiliated Institutions of the University.

UNIT-II

Review of image processing techniques dassical filtering operations thresholding techniques - edge
detection techniques - corner and interest point detection -mathematical morphology - texture.
UNIT-IV

Binary shape analysis - connectedness - object labelling and counting - size filtering - distance functions -
skeletons and thinning- deformable shape analysis boundary tracking procedures -active contours- shape
models and shape recognition centroidal profiles - handling occlusion - boundary length measures
boundary descriptors- chain codes- Fourier descriptors -region descriptors - moments.
Textbook(s):
1. Patterm Classification, Richard O. Duda, Peter E. Hart, and David G. Stork. Wiley, 2000, 2nd Edition
2. D. L. Baggio et al., Mastering OpenCV with Practical Computer Vision Projects, Packt Publishing, 2012.

References:
1. Pattern Recognition, Jürgen Beyerer, Matthias Richter, and Matthias Nagel. 2018
2. E. R. Davies, Computer & Machine Vision, Fourth Edition, Academic Press, 2012
 Handbook of B. Tech. Programmes offered by USICT at Affiliated Institutions of the University.

Machine Learning P C
3 3

Discipline(s) /EAE / OAE Semester Group Sub-group Paper Code

ECE 6 PCE PCE-3 ECE-350T
EAE MLDA-EAE MLDA-EAE-2C ML-342T
CSE/IT/CST/TE 7 PCE PCE-5 CIE-421T
CSE-AIML 7 PC PC ML-407T
EAE 7 AIML-EAE AIML-EAE-3 ML-407T

Marking Scheme:
1. Teachers Continuous Evaluation: 25 marks
2. Term end Theory Examinations: 75 marks
Instructions for paper setter:
1. There should be 9 questions in the term end examinations question paper.
2. The first (1st) question should be compulsory and cover the entire syllabus. This question should be
objective, single line answers or short answer type question of total 15 marks.
3. Apart from question 1 which is compulsory, rest of the paper shall consist of 4 units as per the syllabus.
Every unit shall have two questions covering the corresponding unit of the syllabus. However, the student
shall be asked to attempt only one of the two questions in the unit. Individual questions may contain upto
5 sub-parts /sub-questions. Each Unit shall have a marks weightage of 15.
4. The questions are to be framed keeping in view the learning outcomes of the course/paper. The standard
/level of the questions to be asked should be at the level of the prescribed textbook.
5. The requirement of (scientific) calculators/ log-tables / data -tables may be specified if required.
Course Objectives :
1 To understand the need of machine learning
2 To learn about regression and feature selection
3 To understand about classification algorithms
4 To learn clustering algorithms
Course Outcomes (CO)
CO 1 To formulate machine learning problems
CO 2 Learn about regression and feature selection techniques
CO 3 Apply machine learning techniques such as classification to practical applications
CO 4 Apply clustering algorithms
Course Outcomes (co) to Programme Outcomes (PO) mapping (scale 1: low, 2: Medium, 3: High)
PO01 PO02 PO03 PO04 POOS PO06 PO07 PO08 PO09 PO10 PO11 P012
CO 1 3 3 3 3 3 2 2
CO 2 3 3 3 2 -
2
CO 3 3 3 3 3 3 2 2 - - -
2
CO 4 3 3 3 3 2

UNIT-I

Introduction: Machine learning, terminologies in machine learning, Perspectives and issues in machine
learning, application of Machine learning, Types of machine learning: supervised, unsupervised, semi
supervised learning. Review of probability, Basic Linear Algebra in Machine Learning Techniques, Dataset and
its types,Data preprocessing, Bias and Variance in Machine learning, Function approximation, Overfitting

UNIT-I|

Regression Analysis in Machine Learning: Introduction to regression and its terminologies,Types of

regression, Logistic Regression

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Handbook of B. Tech. Programmes offered by USICTat AffiliatedInstitutions of the University.

Simple Linear regression: Introduction to Simple Linear Regression and its assumption, Simple Linear
Regression Model Building,Ordinary Least square estimation, Properties of the least-squares estimators and
the fitted regression model, Interval estimation in simple linear regression , Residuals
Multiple Linear Regression:Multiple linear regression model and its assumption, Interpret Multiple Linear
Regression Output(R-Square, Standard error, F, Significance F, Cofficient P values), Access the fit of multiple
linear regression model (R squared, Standard error)
Feature Selection and Dimensionality Reduction: PCA, LDA, ICA

UNIT-I|

Introduction to Classification and Classification Algorithms: What is Classification? General Approach to

Classification, k-Nearest Neighbor Algorithm, Random Forests, Fuzy Set Approaches
Support Vector Machine: Introduction, Types of support vector kernel - (Linear kernel, polynomial kernel, and
Gaussiankernel), Hyperplane - (Decision surface), Properties of SVM, and Issues in SVM.
Decision Trees: Decision tree learning algorithm,ID-3algorithm, Inductive bias, Entropy and information
theory, Information gain,Issues in Decision tree learning.
Bayesian Learning - Bayes theorem, Concept learning, Bayes Optimal Classifier, Naive Bayes classifier,
Bayesian belief networks, EM algorithm
Ensemble Methods: Bagging, Boosting and AdaBoost and XBoost,
Classification Model Evaluation and Selection: Sensitivity, Specificity, Positive Predictive Value, Negative
 Instructions for paper setter:
1. There should be 9 questions in the term end examinations question paper.
2. The first (1st) question should be compulsory and cover the entire syllabus. This question should be
objective, single line answers or short answer type question of total 15 marks.
3 Apart from question 1 which is compulsory, rest of the paper shall consist of 4 units as per the syllabus.
Every unit shall have two questions covering the corresponding unit of the syllabus. However, the student
shall be asked to attempt only one of the two questions in the unit. Individual questions may contain upto
5 sub-parts / sub-questions. Each Unit shall have a marks weightage of 15.
4 The questions are to be framed keeping in view the learning outcomes of the course / paper. The standard
llevel of the questions to be asked should be at the level of the prescribed textbook.
5. The requirement of (scientific) calculators / log-tables / data - tables may be specified if required.
Course Objectives
1 To understand the need of machine learning
2 To learn about regression and feature selection
3. To understand about classification algorithms
4 To learn clustering algorith ms
Course Outcomes (co)
CO 1 To formulate machine learning problems
CO 2 Learn about regression and feature selection techniques
CO 3 Apply machine learning techniques such as classification to practical applications
CO 4 Apply clustering algorithms
Course Outcomes (CO) to Programme Outcomes (PO) mapping (scale 1: low, 2: Medium, 3: High)
PO01 PO02 PO03 PO04 POO5 PO06 PO07 PO08 PO09 PO10 PO11 PO12
CO 1 3 3 3 3 3 2 2
CO 2 3 3 3 3 3 2 -
2
CO 3 3 3 3 2 2
CO 4 3 3 2

UNIT-I

Introduction: Machine learning, terminologies in machine learning, Perspectives and issues in machine
learning, application of Machine learning, Types of machine learning: supervised, unsupervised, semi
supervised learning. Review of probability, Basic Linear Algebra in Machine Learning Techniques, Dataset and
its types,, Data preprocessing, Bias and Variance in Machine learning, Function approximation, Overfitting
UNIT-I|

Regression Analysis in Machine Learning: Introduction to regression and its terminologies, Types of
regression, Logistic Regression

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Handbook of B. Tech. Programmes offered by USICT at Affiliated Institutions of the University.

Simple Linear regression: Introduction to Simple Linear Regression and its assumption, Simple Linear
Regression Model Building,Ordinary Least square estimation, Properties of the least-squares estimators and
the fitted regression model, Interval estimation in simple linear regression, Residuals
Multiple Linear Regression:Multiple linear regression model and its assumption, Interpret Multiple Linear
Regression Output(R-Square, Standard error, F, Significance F, Cofficient P values), Access the fit of multiple
linear regression model (R squared, Standard error)
Feature Selection and Dimensionality Reduction: PCA, LDA, ICA

UNIT-I|

Introduction to Classification and Classification Algorithms: What is Classification? General Approach to

dassification, k-Nearest Neighbor Algorithm, Random Forests, Fuzzy Set Approaches
Support Vector Machine: Introduction, Types of support vector kernel -(Linear kernel, polynomial kernel, and
Gaussiankernel), Hyperplane -(Decision surface), Properties of SVM, and lssues in SVM.
Decision Trees: Decision tree learning algorithm,lD-3algorithm, Inductive bias, Entropy and information
theory, Information gain,Issues in Decision tree learning.
Bayesian Learning - Bayes theorem, Concept learning, Bayes Optimal Classifier, Naive Bayes classifier,
Bayesian belief networks, EM algorithm
Ensemble Methods: Bagging, Boosting and AdaBoost and XBoost,
dassification Model Evaluation and Selection: Sensitivity, Specificity, Positive Predictive Value, Negative
Predictive Value, Lift Curves and Gain Curves, ROC Curves, Misclassification Cost Adjustment to Reflect Real
World Concerns, Decision Cost/Benefit Analysis

UNIT -IV

Introduction to Cluster Analysis and Clustering Methods: The Clustering Task and the Requirements for
duster Analysis, Overview of Some Basic Clustering Methods:-k-Means Clustering, k-Medoids Clustering,
Density-Based Clustering: DBSCAN - Density-Based Clustering Based on Connected Regions with High Density,
Gaussian Mixture Model algorithm , Balance lIterative Reducing and Clustering using Hierarchies (BIRCH)
Affinity Propagation clustering algorithm, Mean-Shift clustering algorithm, ordering Points ldentify the
dustering Structure (OPTICS) algorithm, Agglomerative Hierarchy clustering algorithm, Divisive Hierarchical,
Measuring Clustering Goodness

Textbook(s):
1. Tom M. Mitchell, "Machine Learning', McGraw-Hill Education (India) Private Limited, 2013.
2. M. Gopal, "Applied Machine Learning", McGraw Hill Education

References:
1. C. M. BISHOP (2006), "Pattern Recognition and Machine Learning", Springer-Verlag New York, 1st Edition
| 2. R. O. Duda, P. E. Hart, D. G. Stork (2O00), Pattern Classification, Wiley-Blackwell, 2nd Edition