Syllabus for
Master of Science (Artificial Intelligence and Machine Learning)
Academic Year  (2023)

Machines are gaining more intelligence to perform human like tasks.  Artificial Intelligence has spanned across the world irrespective of domains. MSc (Artificial Intelligence and Machine Learning) will enable to capitalize this wide spectrum of opportunities to the candidates who aspire to master the skill sets with a research bent. The curriculum supports the students to obtain adequate knowledge in the theory of artificial intelligence with hands-on experience in relevant domains with tools and techniques to address the latest demands from the industry. Also, candidates gain exposure to research models and industry standard application development in specialized domains through guest lectures, seminars, industry offered electives, projects, internships, etc.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: Apply programming principles and practices for developing automation solutions to meet future business and society needs.

PO3: Ability to use or develop the right tools to develop high end intelligent systems

PO4: Adopt professional and ethical practices in Artificial Intelligence application development

PO5: Understand the importance and the judicious use of technology for the sustainability of the environment.

Programme Specific Outcome:

Programme Educational Objective:

Continuous Internal Assessment: 50% Weightage

End Semester Examination: 50% Weightage

This course aims to provide fundamental knowledge of mathematical foundations for computational Intelligence in Artificial Intelligence and Machine Learning. 

Systems of Linear Equations-Row reduction and Echelon Forms-Vector Equations-Matrix Equation-Solution Sets of Linear Systems-Applications of Linear Systems-Linear Independence-Introduction to Linear Transformations-The Matrix of Linear Transformation-Linear Models in Business, Science and Engineering

Matrix Operations-The Inverse of a Matrix-Characterizations of Invertible Matrices-Partitioned Matrices-Matrix Factorizations-The Leontief Input-Output Model- Application to Computer Graphics-Subspaces OF RN-Dimension and Rank

Vector Spaces and Subspaces-Null Spaces, Column Spaces and Linear Transformations-Linearly Independent Sets; Bases- Coordinate Systems-The Dimension of a Vector Space-Rank-Change of Basis-Applications to Difference Equations-Application to Markov Chains.

Eigenvectors and Eigenvalues-The Characteristics Equation-Diagonalization-Eigenvectors and Linear Transformations-Complex Eigenvalues-Discrete Dynamical Systems-Application of Differential Equations-Iterative Estimate for Eigenvalues 

Presentation of data using graphs-Computation of central tendency and dispersion-Correlation and Regression-Case studies

Definition of Probability, conditional probability, Total probability theorem, Bayes theorem.

Random Variables: Continuous and discrete random variable-Definition probability mass function- Probability density function - Expectation and variance-Standard discrete distributions-Bernoulli, binomial, Poisson and geometric-Standard continuous distributions-Normal and Exponential.

1. David C. Lay, Steven R. Lay, and Judi J. McDonald, “Linear Algebra and Its Applications”, Pearson, Fifth Edition,2016. 

2. Gupta S.C & Kapoor V.K, “Fundamentals of Mathematical statistics”, Sultan Chand & sons, 2020.

3. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An Introduction to Statistical Learning with Applications in R” Springer, Second Edition,2021

1. Douglas C Montgomery, George C Runger, “Applied Statistics and Probability for Engineers”, Wiley student edition, 2004.

2. RSN Pillai: Statistics Theory and Practice. S. Chand Edition 8,2018

CIA-50%

This course is designed to teach the basic statistical concepts. This will help students to develop an understanding of random variables, probability distributions, and high-dimensional random variables, as well as sampling distributions and inferential statistics.

Data - qualitative and quantitative data: binary, categorical, continuous - measures of central tendency - measures of dispersion – skewness

Random experiment - events - probability - classical definition - addition rule - random variable: discrete and continuous - expectation – bivariate and multivariate random vectors: definition – expectation and covariance matrix (only statement)

Probability distributions for discrete data: Bernoulli - binomial - Poisson – multinomial ,Probability distributions for continuous data: Normal – logistic distribution – multivariate normal: pdf and mean vector and covariance matrix (only statement)

Population and sample - parameter and statistic – sampling error - sampling distributions: chi-square, t, F (only definition and statement of applications) – hypotheses: null and alternative – types of errors – level of significance – p-value - test statistics – critical region

One sample and two sample t-test – ANOVA (only hypothesis, the test statistic and numerical illustration)

Inference for single proportions – Inference for two proportions - testing for the goodness of fit using chi-square – Testing for independence (two-way tables) 

1. Barr, Christopher, David M. Diez, and Cetinkaya Rundel. OpenIntro statistics. (2019).

1. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 12th edition, Sultan Chand & Sons, New Delhi, 2020

2. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, 3rd edition, McGraw Hill, New Delhi, 2017.

CIA-50%

This course aims at developing an understanding about the fundamental concepts in defining and simulating perception, identifying the problems where AI is required. And the different AI techniques available to define and explain learning algorithms

Introduction to AI, The Foundations of AI, AI Technique -Tic-Tac-Toe. Problem characteristics, Production system characteristics, Production systems: 8-puzzle problem.

Intelligent Agents: Agents and Environments, Good Behavior: The concept of rationality – The nature of Environments, The Structure of Agents -Expert Systems-Types of Expert Systems

Searching: Uninformed search strategies – Breadth first search, depth first search. Generate and Test, Hill climbing, simulated annealing search, Greedy best first search, A* search, AO* search

Propositional logic - syntax & semantics - First order logic. Inference in first order logic, propositional Vs. first order inference, unification & lifts, Clausal form conversion, Forward chaining, Backward chaining, Resolution

Ethical Considerations on AI – bias – privacy – philosophical challenge in human judgement – faulty algorithms - Social Implications of AI – Case studies Planning and Acting in the Real World

1. E. Rich and K. Knight, Artificial Intelligence, 3rd  Edition, New york: TMH, 2019

2. S. Russell and P. Norvig, Artificial Intelligence A Modern Approach, 3rd  Edition,  Pearson Education, 2019.

1. Eugene Charniak and Drew McDermott, Introduction to Artificial Intelligence, 2nd Edition. Pearson Education, 2005.

2. George F Luger, Artificial Intelligence Structures and Strategies for Complex Problem Solving, 4th Edition. Pearson Education, 2008

3. N.L. Nilsson, Artificial Intelligence: A New Synthesis, 1st Edition. Morgan Kaufmann, 2000

CIA-50%

 The research methodology module is intended to assist students in planning and carrying out research projects. The students are exposed to the principles, procedures and techniques of implementing a research project. The course starts with an introduction to research and carries through the various methodologies involved. It continues with finding out the literature using computer technology, basic statistics required for research and ends with linear regression. 

 Defining research problem - selecting the problem - necessity of defining the problem - techniques involved in defining a problem- Ethics in Research.

Principles of experimental design Working with Literature: Importance, finding literature, using your resources, managing the literature, keep track of references, using the literature, literature review.

 On-line Searching: Database – SCIFinder – Scopus - Science Direct - Searching research articles - Citation Index - Impact Factor -  H-index etc

Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation.

Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, text, tables, figures, equations, citations, referencing, and templates (IEEE style), paper writing for international journals, Writing scientific report.

Latex: Introduction-Text-Tables- Figures- Equations- Citations- Referencing and Templates (IEEE style).

 [2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.  

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010.  a

CIA-50%

This course is designed to introduce the principles and design of machine learning techniques. This course aims to provide foundations for conceptual aspects of machine learning algorithms along with their applications to solve real world problems.

 Getting to Know your data: Data Objects and Attribute Types, Measuring Data Similarity and Dissimilarity – Data Preprocessing: An Overview – Data Cleaning – Data Integration – Data Reduction – Data Transformation – Data Discretization. 

 INTRODUCTION TO MACHINE LEARNING:

Origins of Machine Learning – Basic learning process – Machine Learning in Practice – Types of Machine Learning Algorithms

 Lab Exercises:

1. Data Exploration for identifying different datasets

 2. Preprocessing the dataset using normalization techniques 

Mining Frequent Patterns, Associations and Correlations - Basic Concepts - Frequent Itemset Mining Methods – Pattern Evaluation Methods

 Lab Exercises:

1. Identify frequent itemsets using Apriori Algorithm

 2. Generate FP Tree for a transaction dataset 

Pattern Mining – Pattern Mining in Multilevel, Multidimensional space – Constraint-based Frequent Pattern Mining – Mining High-Dimensional Data and Colossal Patterns – Mining Compressed or Approximate Patterns – Pattern Exploration and Application

 Lab Exercises:

1. Explore generating multilevel association rules 

 2. Explore multidimensional associations 

Classification – Basic Concepts – Decision Tree Induction – Bayes Classification Methods – Rule-Based Classification – Model Evaluation and Selection – Techniques to improve Classification Accuracy.

 Lab Exercises:

1. Demonstrate Naïve Bayes classifier

 2. Construct Decision Tree for a dataset and identify the order of attributes 

Bayesian Belief Networks – Support Vector Machines – Classification using Frequent Patterns – Lazy Learners – Self Study: Additional topics regarding classification

 Lab Exercises:

1. Explore SVM Classifier

 2. Demonstrate Lazy Learner

[2] Introduction to Machine Learning, E. Alpaydin,  3rd Edition, MIT Press, 2014.

[3] Machine Learning with R: Expert techniques for predictive modeling, Brett Lantz, 3rd Edition, Packt Publishing, 2019

[2] Data Mining Techniques, Arun K Pujari, Second Edition, Universities Press India Pvt. Ltd. 2010

Note: Python libraries like MLxtend and Scikit Learn can be used for lab exercises

CIA-50%

To provide a strong foundation for database application design and development by introducing the fundamentals and advanced concepts of database technologies.

Data models, schemas and instances, DBMS architecture and data independence, Database languages and interfaces, database system environment, and Classification of DBMS. Using High-Level Conceptual Data Models for Database Design - Entity Types, Entity Sets, Attributes, and Keys - Relationship Types, Relationship Sets, Roles, and Structural Constraints, Enhanced Entity Relationship Model - SQL Data Definition and Data Types, Specifying Constraints in SQL, Basic Retrieval Queries in SQL, Additional features of SQL. Complex Queries, Triggers, Views, and Schema Modification More Complex SQL Retrieval Queries, Specifying Constraints as Assertions and Actions as Triggers, Views (Virtual Tables) in SQL, Schema Change Statements in SQL.

Lab Exercises:

1. DDL, DML, and TCL commands

 2. Use of integrity constraints and referential integrity.

Design Guidelines for Relation Schemas - Functional Dependencies - Normal Forms Based on Primary Keys - Second and Third Normal Forms - Boyce-Codd Normal Form – Multivalued Dependency and Fourth Normal Form - Join Dependencies and Fifth Normal Form – Inference Rules, Equivalence and Minimal Cover - Properties of Relational Decompositions - Nulls and Dangling Tuples - File Organization - Organization of Records in Files - Ordered Indices - B+ Tree Index Files - Static Hashing - Bitmap Indices.

Lab Exercises:

3. Data Retrieval using JOINS 

 4. Subqueries and Correlated queries

Transaction - Introduction to transaction processing- transaction and system concept- Desirable properties of the transaction- Transaction support in SQL- concurrency control techniques – Two-phase Locking techniques for concurrency- Concurrency Control Based on Timestamp Ordering. Recovery Concepts- NO-UNDO/REDO Recovery Based on Deferred Update- Recovery Techniques Based on Immediate Update- Shadow Paging.

Lab Exercises:

5. Views in SQL

 6. Stored Procedures and Triggers

Distributed databases: Distributed Database concepts- Types - Data Fragmentation- Replication- Allocation Techniques. Overview of Transaction Management - Overview of Concurrency Control and Recovery.NOSQL Databases-Introduction to NOSQL Systems, The CAP Theorem, Document-Based NOSQL Systems and MongoDB, NOSQL Key-Value Stores, Column-Based or Wide Column NOSQL Systems, NOSQL Graph Databases.

Lab Exercises:

7.  NOSQL CRUD operations

 8. .NOSQL Aggregate functions

Accessing Data from Column-Oriented Databases Like HBase-Querying Redis Data stores- Querying in Neo4J-Changing Document Databases-Schema Evolution in Column-Oriented Databases-HBase Data Import and Export-Data Evolution in Key/Value Stores-Map-Reduce- Basic Map-Reduce-Map-Reduce Calculations-2 stage example. Indexing and Ordering Data Sets-Essential Concepts Behind A Database Index-Indexing and Ordering in MongoDB-Creating and Using Indexes in MongoDB-Indexing and Ordering in CouchDB-Indexing in Apache Cassandra-Indexing and Ordering in Neo4J.

Lab Exercises:

9.  NoSQL data  IMPORT and EXPORT  

 10. MAP-REDUCE  in NoSQL

 [2] Shashank Tiwari, Professional NoSQL, Wrox Press, Wiley, 2021, ISBN: 978-0-470-94224-6

[2] O’neil Patric, O’neil Elizabeth, Database Principles, Programming and Performance, Argon Kaufmann Publishers, 2nd Edition, 2002. 

[3] Ramakrishnan and Gehrke, Database Management System, McGraw-Hill, 3rd Edition, 2003.

[4] Gaurav Vaish, Getting Started with NoSQL, Packt Publishing, 2013.

CIA-50%

Data visualization techniques allow people to use their perception to better understand the data. The goal of this course is to introduce students to data visualization which includes principles and techniques. 

 Students will learn the value of visualization, specific techniques in information visualization and scientific visualization.  

 Value of Visualization – What is Visualization and Why do it: External representation – Interactivity – Difficulty in Validation. Data Abstraction: Dataset types – Attribute types – Semantics. Task Abstraction – Analyze, Produce, Search, Query. 

 Four levels of validation – Validation approaches – Validation examples. Marks and Channels. Rules of thumb – Arrange tables: Categorical regions – Spatial axis orientation – Spatial layout density. Arrange spatial data:

 Geometry – Scalar fields – Vector fields – Tensor fields. Arrange networks and trees: Connections, Matrix views – Containment. Map color: Color theory, Color maps and other channels.

 Manipulate view: Change view over time – Select elements – Changing viewpoint – Reducing attributes. Facet into multiple views: Juxtapose and Coordinate views 

 Partition into views – Static and Dynamic layers – Reduce items and attributes: Filter – Aggregate. Focus and context: Elide – Superimpose – Distort – Case studies.

1. Tamara Munzner, Visualization Analysis and Design, A K Peters Visualization Series, CRC Press, 2014.

2. Scott Murray, Interactive Data Visualization for the Web, O’Reilly, 2013.

1. Alberto Cairo, The Functional Art: An Introduction to Information Graphics and Visualization, New Riders, 2012

2. Nathan Yau, Visualize This: The FlowingData Guide to Design, Visualization and Statistics, John Wiley & Sons, 2011.

CIA-50%

To provide a foundational knowledge of data engineering and knowledge representation. 

To store , retrieve, analyze and design data for various applications  

To represent different sorts of knowledge, such as uncertain or incomplete knowledge

Data Engineering 

Introduction to Data Engineering - Data Engineering versus Data Science – Data Engineering tools– Data Engineering Lifecycle 

Data Models

Data Systems – Reliability – Scalability – Maintainability -Data Models and Query Languages. - Relational Model Versus Document Model - Query Languages for Data -Query Languages for Data,Declarative Queries on the Web ,MapReduce Querying ,Graph-Like Data Models Property Graphs ,The Cypher Query Language ,Graph Queries in SQL ,Triple-Stores and SPARQL

Introduction – Data Engineering ecosystem - Building data pipelines—Extract, Transform, Load -ETL Process – Data Structures related to  Database – Other data integration methods – Benefits and Challenges of ETL – ETL tools .Data Warehousing - Stars and Snowflakes: Schemas for Analytics- Column-Oriented Storage - Column Compression -Sort Order in Column Storage - Writing to Column-Oriented Storage .

Data Storage and Retrieval Non Relational data

Non Relational data – NoSQL- Language-Specific Formats JSON, XML, and Binary Variants  - Modes of Dataflow Dataflow Through Databases DATA in Distributed systems.Data in distributed systems – Partitioning and Replication - Partitioning of Key-Value Data - Partitioning and Secondary Trouble with Distributed Systems- Faults and Partial Failures - Unreliable Networks - Unreliable Clocks

Knowledge Representation - Ontological Engineering - Categories and Objects . Events - Mental Events and Mental Objects - Reasoning Systems for Categories -  Reasoning with Default Information Uncertain knowledge and reasoning- Quantifying Uncertainty - Acting under Uncertainty - Basic Probability Notation .

Probabilistic Reasoning-Representing Knowledge in an Uncertain Domain -The Semantics of Bayesian Networks -Efficient Representation of Conditional Distributions -Exact Inference in Bayesian Networks -Relational and First-Order Probability Models 

1. Ted Malaska , Rebuilding Reliable Data Pipelines Through Modern Tools , first edition,  O’Reilly, 2019

2. Paul Crickard, Data Engineering with Python,  first edition, Packt Publishing,2020

3. Ronald J. Brachman, Hector J. Levesque, KNOWLEDGE REPRESENTATION AND REASONING, Elsevier , 2004

4. S.L. Kendal and M. Creen An Introduction to Knowledge Engineering, Springer, 2007

CIA-50%

This core course covers principles of algorithm design, elementary analysis of algorithms, and fundamental data structures. The emphasis is on choosing appropriate data structures and designing correct and efficient algorithms to operate on these data structures.

 Algorithms as technology – Analyzing and Designing algorithms – Asymptotic notations – Recurrences – Methods to solve recurrences – Heap Sort - Quick Sort – Sorting in linear time – Radix sort – Selection in linear time. Introduction: Algorithms, Pseudo code for expressing algorithms, performance analysis Space complexity, Time Complexity, Asymptotic notation- Big oh notation, omega notation and theta notation    

Divide and conquer methodology – Multiplication of large integers – Strassen's matrix multiplication – Greedy method – Prim's algorithm – Kruskal's algorithm – algorithm for Huffman codes, Knapsack problem, Spanning trees, Minimum cost spanning trees, Single source shortest path problem.

Dynamic Programming: General method, applications- Matrix chained multiplication,Optimal binary search trees, 0/1 Knapsack problem, All pairs shortest path problem, Traveling sales person problem, Reliability design.

 Backtrcking: General method, Applications- n-queue problem, Sum of subsets problem, Graph coloring, Hamiltonian cycles.

 Branch and Bound: General method, applications- Travelling sales person problem, 0/1 Knapsack problem- LC branch and Bound solution, FIFO branch and Bound solution. NP-Hard and NP-Complete Problems: Basic concepts, Non deterministic algorithms, NP-Hard and NP Complete classes 

 [2] Design and Analysis of Algorithms, P. h. Dave,2nd Edition, Pearson Education

1. Introduction to the Design And Analysis of Algorithms A Levitin Pearson Education

2. Algorithm Design foundations Analysis and Internet examples, M.T.Goodrich and R Tomassia John Wiley and sons

3. Design and Analysis of Algorithms, S. Sridhar, Oxford Univ.Press

4. Design and Analysis of Algorithms ,Aho , Ulman and Hopcraft , Pearson Education.

5. Foundations of Algorithms, R. Neapolitanand K.Naimipour , 4th edition

CIA-50%

This course is intended to carry out supervised research in a particular domain. The students are expected to identify, formulate and analyze the research problem. The students are also expected to conduct critical review of literature, choosing the study design, deciding on the sample design, become proficient in tools to solve the research problem. Students are expected to adhere research ethical practices at every phase of development and submit the intermediate reports.

The students are expected to carry out the following:

·         Identify the background of research and conduct critical review of literature to understand the context.

 ·         Identification of research gaps

·         Formulate research questions/Objectives and hypothesis based on the research problem.

 ·         Methodology or approach intended to be adopted in the execution of the research

 ·         Expected outcome of research

50%     5O%

This course will help the learner to gain sound knowledge in object-oriented principles, GUI application development, web application development and enterprise application development by using different features of Java technologies.

Introduction to Object Oriented Programming (OOP)

Object-Oriented Programming (OOP) Principles- Class Fundamentals - Declaring Objects - Introducing Methods - Overloading methods – Constructors - Parameterized Constructors - this Keyword.

Class Features

Garbage Collection - the finalize () Method - Introducing Access Control - Understanding static - Introducing nested and inner classes - String class - String Buffer Class - Command Line Arguments

Lab Exercises:

1. Identify a domain of your choice, list out ten entities in the domain. For each entity, identify minimum 10 attributes and assign the data type for each attribute with proper justification.

2. Implement the concept of class, data members, member functions and access specifiers, function overloading and constructor overloading

3. Implement the features of static keyword, command line argument, String class and String Buffer class

Inheritance in Java

Inheritance Basics - Multilevel Hierarchy- Using super - Method overriding - Dynamic Method Dispatch- Abstract keyword- Using final with inheritance - The Object Class.

Interfaces and Packages

Inheritance in java with Interfaces – Defining Interfaces - Implementing Interfaces - Extending Interfaces- Creating Packages - CLASSPATH variable - Access protection - Importing Packages - Interfaces in a Package.

Multithreading Java

Thread Model - Life cycle of a Thread - Java Thread Priorities - Runnable interface and Thread Class- Thread Synchronization – Inter Thread Communication.

Lab Exercises:

4. Implement the concept of inheritance, super, abstract and final keywords.

5. Implement the concept of package and interface.

6. Implement the concept of multithreading.

Generics

Generics Concept - General Form of a Generic Class – Bounded Types – Generic Class Hierarchy - Generic Interfaces – Restrictions in Generics.

Lambda Expression

Introduction to Lambda expression- Block Lambda Expressions - Generic Functional Interfaces - Passing lambda expressions as arguments - Lambda expressions and exceptions- Lambda expressions and variable capture.

The Collections Framework

The Collections Overview – Collection Interface – List Interface – Set Interface – SortedSet Interface – Queue Interface - ArrayList Class – LinkedList Class – HashSet Class – Using an Iterator – The For Each Statement. Working with maps – The map interfaces, the map classes. Comparators- the collection algorithms 

Lab Exercises:

7. Implement the concept of Generics

8. Implement the concept of the lambda expression

9. Implement the concept of a collection framework

JDBC

Introduction to JDBC- Connecting to the database- Basic JDBC Operations – Essential JDBC Classes – JDBC Drivers – JDBC-ODBC Bridge – Connecting to a database with driver manager – JDBC database URL. 

JAVA BEANS 

Java beans - Advantages of Beans – Introspection- Bound and Constrained Properties – Persistence – Customizers - The JavaBeans API. 

JAVA SWING

Swing Basics – Components and Containers – JLabel and ImageIcons- JTextField – Swing Buttons – JTabbedPane – JScrollPane – JList – JComboBox – JTable – Swing Menus.

Lab Exercises:

10. Implement the concept of JDBC and Java Beans

11. Implement the features of java swing package

 JAVA SERVLETS

Servlets Basics – Life Cycle of a Servlet –A Simple Servlet - The Servlet API – Servlet Interfaces – Generic Servlet Class- HttpServletRequest Interface – HttpServeltResponse

JSP

The JSP development model – component of jsp page – Page directive – Action – scriptlet – JSP expression, JSP Syntax and semantics, JSP in XML.

Lab Exercises:

12. Implement the concept of java servlets

13. Implement the concept of JSP

[2] The complete reference JSP 2.0, Tata McGraw- Hill, 2nd Edition, Phil Hanna

CIA-50%

This course covers the most popular machine learning algorithms such as regression techniques with a modern outlook focusing on the recent advances and examples. It also aims to provide the foundations for dimensionality reduction techniques and clustering techniques with their applications to solve real world problems.

 Understanding Regression: Simple Linear regression - Ordinary least squares estimation - Gradient Descent - multiple linear regression - Multivariate linear regression – Polynomial regression – Regularization – Ridge and Lasso Regression - Understanding regression trees and model trees - Logistic regression - Bias and Variance Trade-off – Overfitting and underfitting models.

Self-Study: Support Vector Regression, Decision Tree Regression – Random Forest Regression

Lab Exercises:

1. Implement various types of linear regression techniques

 2. Explore non-linear regression techniques

 Factor Analysis, Low Variance Filter, High Correlation Filter, Backward Feature Elimination – Forward Feature Selection – Principal Component Analysis – Factor Analysis – Multidimensional Scaling - Linear Discriminant Analysis – Independent Component Analysis – Isomap – Maximum Relevance Minimum Redundancy 

Self-Study: - Combining Multiple Learners

 Lab Exercises:

1. Demonstrate Feature selection

 2. Explore and compare PCA, LDA and ICA techniques 

 Cluster Analysis - Partitioning Methods – K-Means – K-Medoids – Hierarchical Methods – Agglomerative Vs Divisive – Distance measures in algorithmic methods – BIRCH – Chameleon – Probabilistic Hierarchical Clustering – Evaluation of Clustering: Assessing clustering Tendency – Determining the Number of Clusters – Measuring Clustering Quality

Lab Exercises:

1. Demonstrate K-Means algorithm with optimum number of clusters 

2. Demonstrate Hierarchical clustering

 3. Evaluate quality of clusters

 Introduction – Single State Case: K-Armed Bandit – Elements of Reinforcement Learning – Model-Based Learning – Temporal Difference Learning – Generalization – Partially Observable States

Self-study and Discussion: Case Studies and recent applications. 

Lab Exercises:

1. Explore model based reinforcement learning

 Application scope of Neural Networks – Fundamental Concept of ANN: The Artificial Neural Network – Biological Neural Network – Comparison between Biological neuron and Artificial Neuron – Evolution of Neural Network. Basic models of ANN – Learning Methods – Activation Functions – Importance Terminologies of ANN – Single / Multilayer perceptron

Lab Exercises:

1. Calculate the output of a simple neuron using binary and bipolar sigmoidal activation functions

2. Demonstrate classification using MLP

      [3] Machine Learning with R: Expert techniques for predictive modeling, Brett Lantz, 3 Edition, Packt Publishing, 2019

 [2] Data Mining Concept and Techniques, Jiawei Han, Micheline Kamber, Jian Pie, Morgan and Kauf

 [3] S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd  Edition,2018.  

Note: Scikit learn python library can be used for lab exercises.

ESE-50%

To explore the current and potential applications of AI and various technologies in agriculture, such as crop monitoring, yield prediction, soil analysis, Plant Disease Identification and pest control to improve the agriculture productivity in India

Introduction – Role of AI in advanced farming - Role of IoT in advanced farming - Role of Robotics in advanced farming – Smart Farming – Smart Agriculture – AI in Agriculture - How Data Analytics Transforming Agriculture – Agricultures Data Analytics Benefits – Challenges of AI in Agriculture - Case Study

History of Precision Agriculture – Introduction – Components – Tools and Techniques – Site Specific Crop Management – VRA & VRT – Adoption of Smart Precision Agriculture  - Modern Day Agriculture – Smart Precision Agriculture – Agriculture Digital Farming – Benefits – Soil Management – High Accuracy in Disease prediction, Detection and Control – Application of WSN in Precision Agriculture

Prediction of Crop Yield and Pest Disease Infestation – Prediction system for Cropyield and livestock – Climate Condition Monitoring and Automated Systems – Decision Making system for Crop Selection based on Soil.

Introduction – Data Mining Techniques in Farming – Case Studies in Agricultural Data Mining – Research Challenges – Machine Learning and its Application in Food Processing and Preservation.

Introduction – Smart farming Tools – Technological Advancements – Climate – Smart Agriculture – Evolution of Cutting Edge Technologies that are revolutionizing the Agriculture in India – Smart Farming Applications - Future Scope and Challenges.

[2]Smart farming technologies for sustainable Agricultureal Development, Poonia, Ramesh C., ‎Gao, Xiao-Zhi, ‎Raja, Linesh,IGI Global, 2018

[2]AI, Edge and IoT-based Smart Agriculture, Ajith Abraham, ‎Sujata Dash, ‎Joel J.P.C. Rodrigues, Academic Press, 2021,Agriculture 5.0,Latief Ahmad, ‎Firasath Nabi,CRC Press 2021

CIA-50%

To select suitable ethical principles and commit to professional responsibilities and human values and contribute value and wealth for the benefit of the society. 

Applying AI in cyber security-The evolution from expert systems to data mining and AI-The different forms of automated learning-The characteristics of algorithm training and Optimization-Beginning with AI via Jupyter Notebooks-Introducing AI in the context of cyber security.

Classification-Regression-Dimensionality Reduction-Clustering-Video anomaly detection Natural Language processing (NLP) for Social media analysis-Large-scale image Processing.

How to detect spam with Perceptron’s- Image spam detection with support vector machines (SVMs)-Phishing detection with logistic regression and decision Trees-Spam detection with Naive Bayes-Spam detection adopting NLP

Authentication abuse Prevention-Account Reputation Scoring-User authentication with keystroke Recognition-Biometric authentication with facial recognition.

How to leverage machine learning (ML) algorithms for fraud Detection-How bagging and boosting techniques can improve an algorithm's Effectiveness-How to analyze data with Jupyter Notebook-How to resort to statistical metrics for results evaluation.

[2] Data Privacy Principles and Practice by Natraj Venkataramanan and Ashwin Shriram, CRC Press

1. Security in the Digital Age: Social Media Security threats and Vulnerabilities by Henry A. Oliver, Create Space Independent Publishing Platform.

2. Information Security Governance, Guidance for Information Security Managers by W. KragBrothy, 1st Edition, Wiley Publication.

3. Auditing IT Infrastructures for Compliance By Martin Weiss, Michael G. Solomon, 2nd Edition, Jones Bartlett Learning.

CIA-50%

It is designed to be a challenging course, involving significant independent work, readings, assignments, and projects. It covers structured knowledge representations, as well as knowledge-based methods of problem solving, planning, decision-making, and learning in cognitive system with help of AI models.

Introduction to Knowledge-Based AI – Where KB AI fits into AI as a whole - Cognitive systems: what are they? - AI and cognition: how are they connected

Fundamentals- Semantic Networks - Generate & Test - Means-Ends Analysis - Problem Reduction - Production Systems

Frames - Understanding - Common Sense Reasoning - Scripts - Logic - Planning

 Learning by Recording Cases - Incremental Concept Learning - Classification - Version Spaces & Discrimination Trees- Case-Based Reasoning - Explanation-Based Learning - Analogical Reasoning

Constraint Propagation - Visuospatial Reasoning- Configuration - Diagnosis - Design - Creativity

 Learning by Correcting Mistakes - Meta-Reasoning - AI Ethics

1. Cognitive Computing Recipes: Artificial Intelligence Solutions Using Microsoft cognitive Services and TensorFlow, Adnan Masood and Adnan Hasami, Paper back, Import,March-2019

Artificial Intelligence: A Modern Approach. Stuart J., and Peter Norvig.  2nd ed. Upper Saddle River, N.J.: Prentice Hall/Pearson Education, 2003. ISBN: 0137903952

ESE-50%

The student can understand the Big Data Platform and its Use cases and get an overview of Apache Hadoop. The course will provide HDFS Concepts and Interfacing with HDFS and the student can understand Map Reduce Jobs. It provides knowledge in NOSQL Data Base, Apache Hadoop architecture, ecosystem, and explores related applications including HDFS, Spark, and MapReduce with Hive and Pig.

 Big Data Overview: Data Structures - Analyst Perspective on Data Repositories - State of the Practice in Analytics - Current Analytical Architecture - Drivers of Big Data - Emerging Big Data Ecosystem and a New Approach to Analytics - Key Roles for the New Big Data Ecosystem - Examples of Big Data Analytics.

 NoSQL Definition and introduction - Document databases – MongoDB - Storing data and accessing data from MongoDB - Querying MongoDB - Document store internals - MongoDB reliability and durability - Horizontal scaling - CRUD operations in MongoDB - Creating and using indexes in MongoDB.

 Understanding Key/Value Stores in Memcached and Redis-Eventually Working with Column-Oriented Databases-HBase Distributed Storage Architecture.

Introduction to Hadoop and MapReduce Programming Hadoop Overview, HDFS (Hadoop Distributed File System), Processing– Data with Hadoop, Managing Resources and Applications with Hadoop YARN (Yet Another Resource Negotiator). Introduction to MAPREDUCE Programming: Introduction, Mapper, Reducer, Combiner, Partitioner, Searching, Sorting, Compression

 Introduction to Hive - Hive Architecture - Characteristics - Comparison with RDBMS (Traditional Database) – HIVE modes – HIVE Server2(HS2) - Hive Data Types and File Formats - Hive Data Model - Hive Integration and Workflow Steps -Hive Built-in Functions - HiveQL - HiveQL. Data Definition Language (DDL) - HiveQL. Data Manipulation Language (DML) - HiveQL for Querying the Data - Aggregation - Join - Group by Clause.

 Introduction - Spark and Big Data Analytics Spark - Introduction to Big Data Tool-Spark - Introduction to Data Analysis with Spark - Spark SQL - Using Python Advanced Features with Spark.

[2] Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Professional Hadoop Solutions, Wiley, 2015.

[3] Gaurav Vaish, Getting Started with NoSQL, Packt Publishing,2013.

[4] High performance spark by Holden Karau and Rachel Warren published by O’Reilly Media 2017.

[2] Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop Real-World Solutions Cookbook, Packt Publishing, 2013.

[3] Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013.  

[4] John Sharp, Data Access for Highly-Scalable Solutions: Using SQL, NoSQL, and Polyglot Persistence,Microsoft,2013

ESE-50%

This course will introduce students to the concepts and applications of Augmented Reality and Virtual Reality technologies. Students will learn the fundamentals of AR/VR, the differences between the two, and how to create AR/VR applications. The course will cover a variety of topics such as AR/VR hardware, software, 3D modeling, user interfaces, and interaction design.

Overview of AR/VR, Brief history of AR/VR, Differences between AR and VR, Advantages and Disadvantages of AR/VR, Applications of AR/VR

AR/VR Hardware and Software

AR/VR devices, software and sensors, VR headsets and controllers, AR/VR software platforms, AR/VR development tools.

 Interaction design principles for AR/VR, User interface design for AR/VR, Human factors in AR/VR design

AR/VR Content Creation

 Introduction to 3D modeling, Creating 3D assets for AR/VR, creating models for AR/VR, adding interactivity to AR/VR experiences, Texturing and lighting for AR/VR. Using Unity3D or Unreal Engine for AR/VR development.

Designing intuitive interfaces for AR/VR, User interface considerations for different AR/Vr devices, Developing AR/VR applications, AR/VR programming languages, AR/VR frameworks and libraries, Best practices for AR/VR UI.

AR/VR Deployment  

Testing and debugging AR/VR applications, Deploying AR/VR applications to devices, AR/VR best practices

 AR/VR emerging technologies, potential future applications, challenges and opportunities.

Students will work on their AR/VR projects - AR/VR Project Presentation - Students will present their AR/VR projects to the class - Industry Visit/ Experiential Learning

Publisher: Addison-Wesley Professional Year: 2016

[2] Learning Virtual Reality: Developing Immersive Experiences and Applications for Desktop, Web,

and Mobile Author: Tony Parisi Publisher: O'Reilly Media Year: 2019

[3] Virtual Reality: Concepts and Technologies" by Philippe Fuchs and Guillaume Moreau (2019)

[4] "Augmented Reality: Principles and Practice" by Dieter Schmalstieg and Tobias Hollerer (2016)

[2] VR Development with Unity: Create Immersive VR Experiences with Unity 3D by Jonathan

Linowes

[3] ARKit 101: Creating Augmented Reality Apps by Erin P. Friar

[4] "Creating Augmented and Virtual Realities: Theory and Practice for Next-Generation Spatial

Computing" by Erin Pangilinan, Shaowen Bardzell, and Jeffrey Bardzell (2019)

[5] "Unity Virtual Reality Projects" by Jonathan Linowes (2018)

CIA-50%

To provide extensive knowledge about computer forensic and recognize diverse aspects of forensics science. It is also used to acquire the knowledge to examine and analyze evidence from image, video, email, data, document, mobile and network.

Web-Based Criminal Activity: Interference with Lawful Use of Computer. Malware: Viruses and Worms-DoS and DDoS Attacks- Botnets and Zombie Armies- Spam- Ransomware and the Kidnapping of Information. Theft of Information, Data Manipulation, and Web Encroachment: Traditional Methods of Proprietary Information Theft-Trade Secrets and Copyrights- Political Espionage. Terrorism: Cyberterrorism- Threatening and Harassing Communications-Cyberstalking and Cyber harassment- Cyberbullying.

Image Forensics: Importance of image forensic detection, Active Methods: Digital watermarking, digital signatures. Passive methods: Image source identification, image tamper detection.

Video Forensics: Active approaches, Blind approaches: copy-move, splicing, Frame insertion, frame deletion, frame duplication, frame replacing, frame shuffling

Opening Pandora’s Box of E-Mail-Following the route of e-mail packets- Becoming Exhibit A- Scoping Out E-Mail Architecture: E-mail structures- E-mail addressing- E-mail lingo- E-mail in motion- Seeing the E-Mail Forensics Perspective: Dissecting the message- Expanding headers- Checking for e-mail extras- Extracting e-mail from clients- Getting to know e-mail file extensions- Copying the e-mail- Printing the e-mail- Investigating Web-Based Mail- Searching Browser Files- Looking through Instant Messages

Delving into Data Storage- Finding Digital Cavities Where Data Hides- Extracting Data- Rebuilding Extracted Data- Document Forensics: Finding Evidential Material in Documents: Metadata- Honing In on CAM (Create, Access, Modify) Facts- Discovering Documents.

Mobile Forensics: Keeping Up with Data on the Move- Making a Device Seizure- Cutting-Edge Cellular Extractions- Network Forensics: Mobilizing Network Forensic Power- Identifying Network Components- Saving Network Data.

Wiretap Act-Communications Assistance for Law Enforcement Act-Foreign Intelligence Surveillance Act-Comprehensive Crime Control Act-Electronic Communications Privacy Act and the Privacy Protection Act.

1. Computer Forensics and Cyber Crime an Introduction, Marjie T. Britz, Pearson education, 3rd Edition, 2013.

2. Handbook of Digital Forensics of Multimedia Data and Devices, Anthony T. S. Ho, Shujun Li, Wiley-IEEE Press, 1st edition, 2016. 

3.  Linda Volonino and Reynaldo Anzaldu, Computer Forensics for Dummies, Wiley Publishing, 2nd Edition 2018

1. Photo Forensics, Hany Farid, The MIT Press, 1st Edition, 2019. 

2. Fake Photos, Hany Farid, The MIT Press, 1st Edition, 2019.

3. The Practice of Crime Scene Investigation, John Horswell, CRC Press, 2016.

ESE 50%

The students are expected to reveal the core competency aimed by this course which includes development of effective solution to the chosen problem, deployment of solution and research findings. The students are expected to submit the final report as well as they are expected to defend their research work

The students are expected to carry out the following:

1.  Collect the data to acquire the information required to answer the research issues.
2.  Develop and deploy the novel solution to the identified problem.
3.  Prepare a research report and defend the research work done.
4.  Publish Article in National or International conference/UGC CARE List journal / WoS / Scopus Indexed journal publication.

50%        50%

The main objective of this course is to make students comfortable with the tools and techniques required to handle large datasets. Several libraries and datasets publicly available will be used to illustrate the application of these algorithms. This will help students develop the skills required to gain experience of doing independent research and study.

An overview of ANN, Back Propagation Neural Networks, Deep Feedforward Networks: Deep network for Universal Boolean function representation, classification and Approximation, perceptron Learning, Perceptron with activation functions

Lab Exercises:

1. Demonstrate MLP in Keras/Tensorflow

 2. Demonstrate Deep Feedforward Network

Regularization for Deep models: L2 and L1 Regularization, Constrained Optimization and Under- Constrained, Early Stopping, Parameter Tying and Parameter Sharing, Sparse representations, Dropout

 Lab Exercises:

1. Demonstrate Regularization L1 for Deep learning model

 2. Demonstrate Regularization L2 for Deep learning model

The Convolution Operation, Pooling, Structured Outputs, Variants of convolution, Variants of CNN – ImageNet, Alexnet, VGG16, ResNet, Applications in Computer Vision

Lab Exercises:

1. Demonstrate Convolution Neural Network

 2. Demonstrate VGG16 or ResNet 

Sequence Processing, Unfolding Computational Graphs, Training recurrent networks 

The Long Short-Term Memory (LSTM), Optimization for Long- Term Dependencies, Encoder-Decoder Sequence-to-Sequence processing

Lab Exercises:

1. Demonstrate Recurrent Neural Network 

 2. Demonstrate Short-Term Long Memory (LSTM) 

The architecture of autoencoders - relationship between the Encoder, Bottleneck, and Decoder, how to train autoencoders? Types of autoencoders: Undercomplete autoencoders, Sparse autoencoders, Contractive autoencoders, Denoising autoencoders, Variational Autoencoders

Lab Exercises:

1.      Demonstrate Sparse Autoencoders

2.      Demonstrate Contractive Autoencoders

1. Deep Learning with Python by Francois Chollet. 2nd Edition, Manning Publications Co., 2020 

2. Introduction to Deep Learning by Eugene Charniak. The MIT Press 2019

3. Dive into Deep Learning by Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola. 2019

1. Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville. MIT Press 2016.

ESE-50%

Students who complete this course will gain a foundational understanding in natural language processing methods and strategies. They will also learn how to evaluate the strengths and weaknesses of various NLP technologies and frameworks as they gain practical experience in the NLP toolkits available. Students will also learn how to employ literary-historical NLP-based analytic techniques like stylometry, topic modeling, synsetting and named entity recognition in their personal research.

 Introduction to NLP- Background and overview- NLP Applications -NLP hard Ambiguity- Algorithms and models, Knowledge Bottlenecks in NLP- Introduction to NLTK, Case study. 

Lab Exercises: 

1. Write a program to tokenize text.

 2. Write a program to count word frequency and to remove stop words.

Word Level Analysis: Regular Expressions, Text Normalization, Edit Distance, Parsing and Syntax- Spelling, Error Detection and correction-Words and Word classes- Part-of Speech Tagging, Naive Bayes and Sentiment Classification: Case study.

Lab Exercises:

3. Write a program to program to tokenize Non-English Languages

 4. Write a program to get synonyms from WordNet

N-gram Language Models: N-Grams, Evaluating Language Models -The language modelling problem 

Semantic Analysis and Discourse Processing 

Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure. 

Lab Exercises:

 5. Write a program to get Antonyms from WordNet 

 6. Write a program for stemming non-English words

 Natural Language Generation: Architecture of NLG Systems, Applications. Machine Translation: Problems in Machine Translation-Machine Translation Approaches. Evaluation of Machine Translation systems. Case study: Characteristics of Indian Languages. 

Lab Exercises:

7. Write a program for lemmatizing words Using WordNet 

 8. Write a program to differentiate stemming and lemmatizing words

  Information Retrieval: Design features of Information Retrieval Systems-Classical, Non-   classical, Alternative Models of Information Retrieval – valuation Lexical Resources: Word Embeddings - Word2vec-Glove. 

Unsupervised Methods in Nlp 

Graphical Models for Sequence Labelling in NLP.

 Lab Exercises: 

9. Write a program for POS Tagging.

10. Write a program to implement Word Embeddings. 

 11.  Case study-based program (IBM) or Sentiment analysis or ChatGpt

 [2] Steven Bird, Ewan Klein and Edward Loper, Natural Language Processing with Python, O’Reilly Media, First edition, 2009. 

 [2] Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press,1999.

ESE -50%

The Objective of this course is to cover the basic theory and algorithms that are widely used in computer vision. Develop hands-on experience in using computers to process images for image enhancement, restoration, filtering and feature extraction to recognize objects.

 Fundamentals: Fundamental Steps in Image Processing, Elements of Digital Image Processing System, Image Sampling and Quantization, Basic relationships: Neighbors, Connectivity, Distance Measures between pixels, Image formation model, Grayscale and Color images representation, Introduction to Digital Video.

Lab Exercises:

1. Program to perform Resize, Rotation of binary, Gray-scale and color images using various methods. 

 2. Demonstrate frame extraction from the video and display the color components of the images.

 Spatial Domain  

Gray Level Transformations, point operations, Histogram Processing, Histogram equalization, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters. 

Frequency Domain  

Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening, Frequency Domain Filters, DCT, Homomorphic Filtering

Lab Exercises:

3. Program to implement various image enhancement techniques using Built-in and user defined functions.

4. Program to implement Linear Spatial Filtering using Built-in and user defined functions

 5. Program to implement Low and High Pass Filtering of images in frequency domain.

 A model of the Image Degradation / Restoration Process, Noise Models, Restoration in the presence of Noise, Periodic Noise Reduction by Frequency Domain Filtering. 

Image Compression models: Huffman coding, Run length coding, LZW coding, JPEG.  

Lab Exercises:

6. Program to implement Non-Linear Spatial Filtering using Built-in and user defined functions 

 7. Demonstrate denoising of the images

 Point, Line and Edge detection, Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method. Region Based Segmentation – Region Growing and Region Splitting and Merging. Representation – Chain codes, Polygonal approximations using minimum perimeter polygons.    

Lab Exercises:

8. Demonstrate Edge detection using various methods.

 9. Perform frame extraction from the video and display the color components of the images.

 Boundary descriptors – Fourier descriptors, regional descriptors –Topological descriptors.

Introduction to Patterns and Pattern Classes: Minimum distance classifier, K-NN classifier. Object detection and recognition – Face recognition (Eigen faces).

 Lab Exercises:

10. Program to demonstrate Fourier descriptors 

11. Extracting feature descriptors from the image dataset.

 12. Implement image classification using extracted relevant features.

 [2] Digital Image Processing and analysis, B.Chanda, D. DuttaMajumdar, PHI, 1st Edition, 2011.

[2] Fundamental of Digital Image Processing, A.K. Jain, PHI, 4th Edition, 2011. 

[3] Digital Image Processing Using MATLAB, Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, PHI, 2nd Edition, 2017.  

[4] Computer Vision: Algorithms and Applications, Richard Szeliski, Springer Science & Business Media, 2nd Edition, 2022.

ESE -50%