1.      Evaluation Pattern: 50% CIA + 50% ESE

2.      Tutorials / Assignments / Tests / Quiz / Seminar.

3.      Attendance is part of the CIA component.

4.      Minimum percentage to pass in each paper is 50% (CIA + ESE).

The department was established in the year 1990, with a  curriculum in line with industry expectations and research. The department also provides opportunities to work on collaborative projects with industry and international universities, faculty expertise in recent technologies and Alumni support are some of the department highlights. The following programmes are offered by the department;

Undergraduate Programmes
BSc CM-Bachelor of Science (BSc) in Computer Sc, Mathematics / Honours / Honours with Research
BSc CS-Bachelor of Science (BSc) in Computer Sc, Statistics / Honours / Honours with Research
BCA-Bachelor of Computer Applications (BCA) / Honours / Honours with Research

BSc CME-Bachelor of Science (BSc) in Computer Sc, Maths, Electronics
BSc CMS-Bachelor of Science (BSc) in Computer Sc, Maths, Statistics

Postgraduate Programmes

Master of Science (Artificial Intelligence and Machine Learning)
Master of Computer Applications (MCA)
Master of Science (Computer Science and Applications)

Research Programmes
Doctor of Philosophy (PhD) in Computer Science

VISION

The Department of Computer Science endeavors to imbibe the vision of the University “Excellence and Service”. The department is committed to this philosophy which pervades every aspect and functioning of the department.

MISSION

“To develop IT professionals with ethical and human values”. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their careers. The department also molds the students to be socially responsible and ethically sound.

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 capitalise 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 intelligence with hands-on experience in relevant domains with tools and techniques to address the latest demands from the industry.It comprises foundational courses from computer Science and advanced courses in the area of Artificial Intelligence and Machine Learning including Natural Language Processing, Deep Learning, Image processing, Soft Computing and IIoT. Research labs, guest lectures, Seminars, practical courses on latest tools, real time application development, multiple elective streams from specialised domains and interdisciplinary are an integral part of the curriculum. Exposure to handle the complex heterogeneous data to solve societal problems is the most preferred skill set required today.

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:

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.

[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

ETE-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.

ETE - 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. 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.

ETE-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. Martin Kleppmann, Designing Data-Intensive Applications - The Big Ideas Behind Reliable, Scalable,and Maintainable Systems,  first edition, O’Reilly ,2017

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

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

ETE-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.

Backtracking: 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

Universities press

[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

ETE-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

ETE-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

[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.

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

            Edition, Packt Publishing, 2019

ETE-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]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

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

ETE - 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. 

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

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

ETE - 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

ETE - 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

ETE - 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 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

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.

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

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.

AVR 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

ETE - 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.

ETE - 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.

• Collect the data to acquire the information required to answer the research issues.

• Develop and deploy the novel solution to the identified problem.

• Prepare a research report and defend the research work done.

• Publish Article in National or International conference/UGC CARE List journal / WoS / Scopus Indexed journal publication.

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

ETE - 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: 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

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

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

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

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

ETE - 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.

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

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] 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.

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

ETE - 50%

By completing the syllabus, the students will learn basics of Internet of Things (IoT), and its execution using multiple robotic sensors and they will be able to impart knowledge on the infrastructure, sensor technologies and networking technologies of Internet of Things (IoT). They will learn to analyze, design, and develop IoT solutions and to know the use of AI in IoT.

IoT Fundamentals- Definition & Characteristics of IoT - Challenges and Issues - Physical Design of IoT, Logical, Design of IoT - IoT Functional Blocks, Security. IoT Reference Architecture- Control Units – Communication modules – Bluetooth – Zigbee – Wi-fi – GPS- IOT Protocols(IPv6, 6LoWPAN, RPL, CoAP etc..), MQTT, Wired Communication, Power Sources, Technologies behind IoT -Four pillars of IOT paradigm, - RFID, Wireless Sensor Networks, SCADA (Supervisory Controland Data Acquisition), M2M - IOT Enabling Technologies – Big-Data Analytics, Cloud Computing, Embedded Systems.

Introduction to Sensors and Actuator- Sensor and Actuator Characteristics- Primary factors driving the deployment of sensor technology. Generations of IoT sensors-Industrial sensors, First Generation, Advanced Generation, Integrated IoT Sensors –Sensors' Swarm, Printed Electronics, IoT Generation Roadmap. Introduction to different IoT tools, Developing application through IoT tools, developing sensor based application through embedded system platform, implementing IoT concepts with python.

Introduction to Arduino and Raspberry Pi- Controlling Hardware- Connecting LED, Buzzer, Controlling AC Power devices with Relays, Controlling servo motor, speed control of DC Motor, Sensors- Light sensor, Temperature and Humidity Sensor DHT11, Motion Detection Sensors etc. Introduction to ESP32/8266- programming with micro-python and connectivity with cloud using wi-fi module

IoT Architecture and Development model of Edge Computing

IoT General Architecture, IoT architecture with AI, Issues and Challenges in IoT with cloud AI, Revised IoT architecture with Fog AI and Edge AI, Tiny ML working principle, Tiny ML as SaaS model, High Computing Machine based architecture, Distributed Training, Compression techniques. Case Study e.g. Water management in public utilities.

IoT Programming Approaches: Node-Centric Programming - Database approach - Model-Driven Development - IoT Programming Frameworks: Contiki and Cooja, Communication Technologies for Low Power WirelessInteractions e.g. RPL and analysis through simulator. Introduction to Wireshark and Foren6 as data analytical tools.

[1]             Tsiatsis, Vlasios, Tsiatsis, Vlasios, Stamatis Karnouskos, Jan Holler, David Boyle, and Catherine Mulligan, Internet of Things: technologies and applications for a new age of intelligence, 2nd edition, Academic Press, 2018. 

[2]             DiMarzio J. F., Beginning Android Programming with Android Studio, 4th edition., Wiley,2016

[2]             Simone Cirani, Gianluigi Ferrari, Marco Picone, Luca Veltri. Internet of Things: Architectures, Protocols and Standards, 1st edition, Wiley Publications, 2019.

[3]             Peter Warden and Daniel Situnayake. Tiny ML: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers, o'reilly, January 2020

Note: Unit 1 & Unit 2 Textbook 2, Unit 3 Textbook 1, Unit 4 Textbook 3 and for Unit 5 web resources are preferable

ETE-50%

Course Objectives

 This course introduces the fundamental concepts and techniques in Artificial Intelligence and Machine Learning. It covers both theoretical aspects and practical applications through hands-on labs.

Introduction to Intelligent Agents – Examples of Agents – Intelligent Agents – Agents and Objects – Agents and Expert systems – Architecture for Intelligent Agents – Belief-Desire-intention Architecture – Layered Architecture – InteRRaP - Applications

Introduction – Background – From Intelligent Agents to Multi Agents – From Multi Agent Systems to Multiagent organization – Sources of Inspiration – Autonomy and Regulation – Examples – Multiagent Organization – OperA Framework – Institutions – Events and states – Example of Institutional modelling – InstAL – Agents in organizations.

Conceptual Foundations of Communication in Multi agent systems – Communicative Acts – Agent Communication Primitives – Commitment based multiagent approaches – Commitment – Commitment Protocol Specification – Evaluation with respect to multiagent systems – Engineering with Agent Communication – Programming with communications – Modelling communications – Communication based methodologies – Advanced topics and challenges

Computational representation of trust values - Trust and Reputation as beliefs – Trust Process in Multiagent System – general overview – Trust Evaluations – Trust Decisions – Coping up with diversity of trust models – Reputation in multiagent societies – Reputation building process – Centralized and Decentralized models – Using reputation – Pitfalls when using reputation – Combining trust and reputation models with other agreement technologies.

Introduction – Challenges in Multiagent Learning – Reinforcement Learning for Multiagent Systems – Evolutionary Game theory as a Multiagent learning paradigm – Swarm Intelligence as a Multiagent Learning paradigm – Neuro Evolution as a Multiagent Learning paradigm – Case study 

Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations by Yoav Shoham, Kevin Leyton-Brown · 2008, Cambridge University Press

Multiagent Systems, Gerhard Weiss, The MIT Press, Cambridge, Massachusetts, London, England, 2^nd Edition, 2013

ETE-50%

This course is designed to provide students with real-world project development and deployment environments.

Specialization project

ETE 50%

This course presents a top–down view of cloud computing, from applications and administration to programming and infrastructure. Students will gain knowledge on the state–of–the–art solutions for cloud computing developed by Google, Amazon, Microsoft, etc. Students will utilize existing Machine Learning based Cloud services to solve relevant problems.

Overview – Characteristics – Deployment Models – Service Models – Cloud Architecture – Cloud Service Delivery Platforms

Virtualization – Load Balancing – Scalability & Elasticity Deployment – Replication – Monitoring – Service Level Agreements – Security – Billing.

Lab Exercises:

1. Demonstration of Cloud Service Delivery Platforms

2. Creating and running compute machines using AWS/GCP/Azure

Identity and Access Management Services – Compute Services – Amazon Elastic Compute Cloud – Google Compute Engine – Windows Azure Virtual Machines – Storage Services – Amazon Simple Storage Service – Google Cloud Storage – Database Services – Amazon Relational Data Store – Amazon DynamoDB – Google Cloud SQL – Google Cloud Datastore.

Lab Exercises:

1. Demonstration of Autoscaling and Load Balancing mechanisms in Cloud

2. Working with IAM

3. Block storage and Object storage

4. Build DB Server (RDMS and NoSQL)

Machine Learning – Types of ML – Business Use Cases – ML Pipeline – Python Tools and Libraries – Machine Learning Frameworks and infrastructure – ML managed Services – ML Challenges – Amazon SageMaker – Implementing a ML pipeline with Amazon SageMaker.

Lab Exercise:

1. Creation of models using ML Pipeline Cloud Services

Amazon Forecast: Overview – Processing Timeseries Data – Amazon Forecast Workflow – Forecast Algorithms – Evaluating Forecast.

Computer Vision: Overview – Image and Video Analytics – Facial Recognition – Amazon Rekognition – Preparing Custom Datasets for Computer Vision – Labelling images with Amazon Ground Truth.

Lab Exercises:

1. Perform Video Analysis using Cloud Recognition Service

2. Implement Forecasting Service

Overview – NLP Managed Services: Amazon Transcribe – Amazon Comprehend – Amazon Polly – Amazon Translate – Cloud Natural Language API.

Lab Exercises:

1. Create a Chatbot using NLP cloud services

2. Analyze text, identify entities, Extract information using NLP API

1. Arshdeep Bahga and Vijay Madisetti, Cloud computing – A Hands–on Approach, Create Space Independent Publishing Platform, Reprint 2018.

2. Stephen Baron, AWS: The Complete Beginner's Guide to Mastering Amazon Web Services, United States, Amazon Digital Services LLC – KDP, 2020.

3. (Note: Cloud services will be used from AWS / GCP as a part of Industry Academia Collaboration)

1. Judith S. Hurwitz and Daniel Kirsch, Cloud Computing for Dummies, 2nd Edition, 2020. 

2. AWS Academy Machine Learning Foundation Modules, AWS, 2023.

3. Google Cloud Platform Associated Qwiklabs, 2020.

ETE-50%

This course aims to explore the principles and applications of Large Language Models (LLMs). It covers the foundations of language models, implementation of conversational agents, transformers in natural language processing, domain-specific models, and advanced topics in large language models.

Overview of language models and their significance-Key Characteristics of LLMs-Types of language models: discriminative vs. generative-Historical evolution of language models-Role of language models in natural language understanding-Introduction to LangChain and its role in LLM development-Key features and components of LangChain-Setting up the LangChain environment for development-Utilizing transfer learning techniques in language modeling-Concept of prompt engineering in fine-tuning models-Optimizing prompts for specific language tasks-Understanding the impact of prompt formulation on model output

Lab Exercises:

1.Implement transfer learning to fine-tune a language model on a specific dataset.

Self-Learning: NLP Tokenization, Word Embeddings and Sequence Modelling

Basic understanding of transformer model architecture-Components like self-attention and feed-forward layers-Simple illustration of transformer operations-Use cases of transformers in language tasks-Basics of using transformers for text classification-Overview of pre-trained transformer models-Introduction to using transformers for text generation-Basic techniques for generating creative text with transformers

Lab Exercises:

2.Develop a simple transformer model using a basic deep learning library.

3.Implement a basic text generation task using transformers.

Evolution of NLP models and the significance of transformers-Key advantages of transformers over traditional methods-Challenges and limitations in implementing transformers-In-depth exploration of the transformer architecture-Mathematical foundations behind transformer operations-Introduction to BERT and its significance in NLP-Bidirectional context representation in BERT-BERT for sequence classification-BERT for token classification-BERT for question/answering-Overview of RoBERTa as an optimization of BERT-Key differences and improvements in RoBERTa architecture-Comparative analysis of BERT and RoBERTa in various NLP tasks

Lab Exercises:

5.Implement fine-tuning procedures on BERT for sentiment analysis.

6.Implement BERT for token classification in NER tasks.

7.Develop a sequence-to-sequence transformer model for language translation using PyTorch.

Understanding the need for domain-specific language models-The role of fine-tuning in creating domain-specific models-Considerations for ethical and responsible use of domain-specific models-Techniques for fine-tuning LLMs for domain-specific applications-An overview of domain adaptation and transfer learning-Integrating user feedback for continuous improvement in domain-specific models-Leveraging prompt engineering in domain-specific model interactions

Lab Exercises:

8.Implement tokenization and encoding for the legal text dataset.

9.Develop a Python script to load the fine-tuned model and tokenizer.

Modern Large Language Models-GPT-3 and ChatGPT-Tailoring large language models for industry-specific use cases-Applications in finance, healthcare, legal, and other sectors-Introduction to multimodal applications integrating text and other modalities-Use of large language models in conjunction with image and audio data-Future directions and challenges in advancing multimodal AI with LLMs-Emerging trends in large language models and AI research-Ethical considerations in shaping the future of AI and LLMs

Lab Exercises:

10.Develop a Python script for handling multimodal inputs.

2. Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs,Ben Auffarth,Packt Publishing,1st edition,2023

1. Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more,Denis Rothman,Packt Publishing,1st edition,2021

2. Introduction to Transformer Models for NLP: Using BERT, GPT, and More to Solve Modern Natural Language Processing Tasks,Sinan Ozdemir,O’Reilly Pearson,2023

CIA = 50%

This course is designed to develop proficiency in advanced data analysis methods, including machine learning algorithms, statistical models, and predictive analytics. This course explores the ethical implications of data analytics, focusing on privacy, bias, and responsible use of data in decision-making processes. This course also analyzes real-world case studies and practical applications of advanced data analytics across different industries, gaining insights into industry best practices.

Introduction to advanced data analytics techniques, Need for Advanced Data Analytics, Statistical Methods for Data Analysis: Time series analysis, Complexities of modern datasets, Recent Technologies and Frameworks for Data Analytics, Role of data analytics in Text, Social Media, and Image. Ethical considerations related to data analytics.

Lab Exercises:

1. Implementation of time series analysis based on web data.

Text Preprocessing- TF-IDF, NER, N-gram modeling. Mining Textual Data: Text Clustering, Text Classification

Lab Exercises:

2. Implementation of text classification and clustering with TF-IDF and   N-gram.

Essentials of Social Graphs, Social Networks, Models, Information Diffusion in Social Media. Analyzing social media: Behavioral Analytics, Influence, and Homophily, Recommendation in social media.

Lab Exercises:

 3. Implementation of user Behavioral Analysis on any social media.

Image Representation and Image Processing- Segmentation-Image Coding and Compression -  Lossless compression versus lossy compression-Measures of the compression efficiency- Hufmann, Recent Trends in Image analytics, Introduction to Video Analytics.

 Lab Exercises:

4. Implementation of Image classification and Image Clustering

5. Compare Compression Techniques.

Predictive Analytics for Patient Admissions - Location-based Case study using GIS, Healthcare: Image Analytics for health image, - 3D image Analytics using CT scans.

[2]. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Third Ed., Prentice-Hall,2020

[2] Fundamentals of Digital Image Processing, Prentice Hall of India,2020

Web Resources:

1. Image Analytics | NIST

2. https://keras.io/examples/vision/3D_image_classification/

ETE-50%

To provide students with a comprehensive understanding of the principles and techniques used in web mining, exploring various approaches to extract knowledge and insights from the vast amount of data available on the web.

Basic concepts of Information Retrieval- Relevance Feedback- Evaluation Measures- Text and Web page pre-processing- Inverted Index and it’s compression- Latent Semantic Indexing- Web Search-Meta Search-Combing Multiple Rankings- Web Spamming

Lab Exercises:

1. Building a Simple Search Engine

2. Web Page Analysis and Spam Detection

Social Network Analysis- Co-citation and Bibliographic Coupling- Page Rank-HITS-Community Discovery

Web Crawling – Basic crawler Algorithm- Implementation Issues- Universal Crawlers- Focused Crawlers- Tropical Crawlers- Evaluation-Crawler Ethics and Conflicts-Some New Developments

Lab Exercises

1. Analyzing Social Networks - Community Discovery and Influence

1. Web Crawling and Spam Detection

Wrapper Induction- Instance Based Wrapper Learning- Automatic Wrapper Generation problems- String matching and Tree Matching- Multiple Alignments- Building DOM Trees- Extraction based on a single list page- Extraction based on Multiple Pages

Information Integration- Introduction to Schema Matching- Pre-processing for schema matching- Domain and Instance level Matching- Combining Similarities- 1:m Match- Integration of Web query interfaces- Constructing a Unified Global Query Interface

Lab Exercises

1. Wrapper Induction for Extracting Product Information

1. Information Integration: Merging Movie Reviews from Different Websites

The Problem of Opinion Mining- Document Sentiment Classification-Sentence Subjectivity and Sentiment Classification- Opinion Lexicon Expansion- Aspect Based Opinion Mining- Mining Comparative Opinions- Opinion Search and Retrieval- Opinion Spam Detection- Utility of Reviews

Lab Exercises

1. Movie Review Sentiment Classification and Analysis

1. Identifying and Combating Opinion Spam

Data Collection and Preprocessing- Data Modelling for Web Usage Mining- Discovery and Analysis of Web Usage patterns- Recommended Systems and Collaborative Filtering- Query Log Mining

Lab Exercises

1. Analyzing User Behavior and Recommending Products

1. Understanding Search Queries and Improving Search

ETE-50%

The aim of Business Intelligence course is to explain the Analytics and Decision Support system in complex business senarios.it aims to illustrate neural network, sentiment analysis, decision making and various data visualization techniques in real time scenarios.

Changing Business Environments and Computerized Decision Support, Managerial Decision Making, Information Systems Support for Decision Making, An Early Framework for Computerized Decision Support, The Concept of Decision Support Systems (DSS), A Framework for Business Intelligence (BI), Business Analytics Overview.

Exercises:

1. Import the legacy data from different sources such as (Excel, SqlServer, Oracle etc.) and load in the target system.

Data Warehousing Process Overview, Data Warehousing Architectures, Data Integration and the Extraction, Transformation, and Load (ETL) Processes, Data Warehouse Development, Real-Time Data Warehousing. Business Reporting Definitions and Concepts, Data and Information Visualization, Different Types of Charts and Graphs, Performance Dashboards.

Exercises:

1. Perform the Extraction Transformation and Loading (ETL) process to construct the database in the Sqlserver / Power BI.

2. Data Visualization from ETL Process.

Basic Concepts of Neural Networks, Developing Neural Network–Based Systems, Support Vector Machines, Text Mining Process, Text Mining Tools, Sentiment Analysis Process, Sentiment Analysis and Speech Analytics, Web Content and Web Structure Mining, Web Usage Mining, Web Analytics Maturity Model and Web Analytics Tools, Social Media Analytics.

Exercises:

1. Creating a Cube using visualization.

2. Apply the what – if Analysis for data visualization. Design and generate necessary reports based on the data warehouse data.

Decision Support Systems Modeling, Structure of Mathematical Models for Decision Support, Decision Modeling with Spreadsheets, Mathematical Programming Optimization, Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking, Decision Analysis with Decision Tables and Decision, Multi-Criteria Decision Making With Pairwise Comparisons, Problem-Solving Search Methods, Genetic Algorithms and Developing GA Applications.

Exercises:

1.Implementation of Classification algorithm.

2. Practical Implementation of Decision Tree.

Big Data and Analytics, Big Data Technologies, Geospatial Analytics, Cloud Computing and BI, Issues of Legality, Privacy, and Ethics, An Overview of the Analytics Ecosystem: Reporting/Analytics, Predictive Analytics, Prescriptive Analytics.

Exercises:

1.Prediction Using Linear Regression

2.Data Analysis using Time Series Analysis

1.  Data Analytics: The Ultimate Beginner's Guide to Data Analytics Paperback – 12 November 2017by Edward Mize

2. Larissa T. Moss, S. Atre, “Business Intelligence Roadmap: The Complete Project Lifecycle of Decision Making”, Addison Wesley, 2003.

3. Carlo Vercelli’s , “Business Intelligence: Data Mining and Optimization for Decision Making”, Wiley Publications, 2009.

4. Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, Bob Becker, “The Data Warehouse Lifecycle Toolkit”, Wiley Publication Inc.,2007

ETE 50%

The course aims to offer foundational understanding of interaction levels, design models, techniques, and validations within human-computer interface and interactions, foster design-oriented thinking for evaluating interactive design.

Good Design and Poor Design – Interaction design – User Experience – Understanding users- Process of Interactive design – Practical Issues – Conceptualizing Interaction 

Cognition – Frameworks – Social Interaction – Face to face – Remote -  Co Presence – Social Engagement – Emotional : Emotions and User Experience – Annoying Interfaces – Affective and Emotional AI – Persuasive Technologies and Behavioual change – Anthropomorphism – Interface Types – Natural User Interfaces 

Data Gathering: – Five key issues – Data Recording – Interviews – Questionnaire-  Observation. Data Analysis and Interpretation: Quantitative and Quantitative – Tools – Presentation .Data at Scale : Approaches – Visualization and Ethical Concern. 

Requirements – Data gathering –Bringing Life – Personas and Scenario – Use cases – Prototype – Conceptual Design – Concrete Design – Generation of Prototypes  

Agile UX – Design patterns – Open Sources – Tools for design – Types of Evaluation – Evaluation Case studies – Issues – Usablity testing – Inspection – Analytics- Predictive Models 

1. Sharp, Rogers and Preece Interaction Design – beyoud Human Computer Interaction Edition 5 , Wiley Publication,2021.

1. Shneiderman, Plaisant, Cohen and Jacobs, Designing the User Interface: Strategies for Effective Human Computer Interaction, 5th Edition, Pearson Publishers, 2010

2. Hans-Jorg Bullinger, ”Human-Computer Interaction”, Lawrence Erlbaum Associates, Publishers

3. Jakob Nielsen,”Advances in Human-computer Interaction”,Ablex Publishing Corporation

4. Thomas S. Huang,” Real-Time Vision for Human-Computer Interaction”, Springer 5. Preece et al, Human-Computer Interaction, Addison-Wesley, 1994

ETE-50%

This course is designed to introduce foundations of graphs, building robust and scalable graph neural networks for complex and deep models. 

Graph Representations – Properties and Measures – Spectral Graph Theory – Graph Signal Processing – Complex Graphs – Computational Tasks on Graphs

Graph Embedding for Simple Graphs – Graph Embedding on Complex Graphs – Graph Neural Networks: - The General GNN Frameworks – Graph filters – Graph Pooling – Parameter learning for Graph Neural Networks 

Graph Adversarial Attacks – Graph Adversarial Defenses – Node-wise Sampling Methods – Layer-wise Sampling methods – Subgraph-wise Sampling Methods

Heterogeneous Graph Neural Networks – Bipartitie Graph Neural networks – Multidimensional Graph Neural Networks – Signed Graph neural networks – Hypergraph Neural networks – Dynamic Graph Neural Networks -Autoencoders on Graphs – Recurrent neural networks on Graphs – Variational Autoencoders on Graphs – Generative Adversarial Networks on Graphs

Graph Neural networks in Natural Language Processing – Computer Vision - Data Mining

[2] https://mitpress.ublish.com/ebook/knowledge-graphs-fundamentals-techniques-and-applications-preview/12636/13

[3] https://mitpress.ublish.com/book/knowledge-graphs-fundamentals-techniques-and-applications

ETE-50%

This course is designed to provide an in-depth understanding of modern optimization techniques and their applications in various domains.

Introduction to Optimization, Convex Optimization, Non-Convex Optimization, Gradient Descent, Newton's Method, Constrained Optimization

Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Ant Colony Optimization, Tabu Search, Differential Evolution

Evolution Strategies, Genetic Programming, Evolutionary Algorithms for Multi-objective Optimization, Evolutionary Optimization for Dynamic Environments

Swarm Intelligence Fundamentals, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony Algorithm, Firefly Algorithm, Bat Algorithm

Optimization in Machine Learning, Engineering Optimization Problems, Optimization in Finance, Optimization in Logistics and Supply Chain Management

Modern Optimization Methods for Science, Engineering and Technology” by G R Singha,Sirajuddin Ahmed and Rajesh Chamorshikar Paperback – Import, 20 November 2019

ETE-50%

This program offers safeguards and instruments designed to protect data and analytics processes against potential threats, unauthorized access, and any malicious activities that could impact them, spanning both online and offline domains. The comprehensive coverage includes the security of data at rest, in transit, during processing, as well as safeguarding big data analytical models and machine learning techniques. 

Privacy-preserving data analysis, Basic Terms - The model of computation, Towards defining private data analysis, Formalizing differential privacy, about differential privacy promises. Threats and attacks for ML systems, Reconstruction attacks, Model inversion attacks

Applying differential privacy in machine learning - Input perturbation, Algorithm perturbation, Output perturbation. Differentially private supervised learning algorithms, Differentially private naive Bayes classification, Implementing differentially private naive Bayes classification, Differentially private logistic regression, Differentially private linear regression

Advanced LDP mechanisms - The Laplace mechanism for LDP, Duchi’s mechanism for LDP, The Piecewise mechanism for LDP, The Piecewise mechanism for LDP, Using LDP naive Bayes with continuous features.

Overview of synthetic data generation- about importance of synthetic data, Application aspects of using synthetic data for privacy preservation, Generating synthetic data, Private information sharing vs. privacy concerns, Using k-anonymity against re-identification attacks

Privacy-preserving machine learning techniques, Privacy protection in data processing and mining, Protecting privacy by modifying the input, Protecting privacy by modifying the input, Privacy-preserving data management and operations.

1. The Algorithmic Foundations of Differential Privacy, Cynthia Dwork and Aaron Roth, Now Publisher, ISBN: 978-1-60198-818-8

2. Differential Privacy: A Primer for a Non-technical Audience, Nissim et al., February 14, 2018

1. Privacy-Preserving Machine Learning Paperback – Import, 22 February 2022 by J. Morris Chang, Di Zhuang, G. Dumindu Samaraweera.

2. Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World Reprint Edition by Bruce Schneier.

3. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity Victor Lobo, Anacleto Correia, June 2022

ETE-50%

This course enables the learners to understand fundamentals of speech recognition, speech production and representation. It also enables the learners to impart knowledge on automatic speech recognition and pattern comparison techniques. This course helps the learners to develop automatic speech recognition model for different applications.

Introduction- The Paradigm for Speech Recognition- Brief History of speech recognition research- The Speech Signal: The process of speech production and perception in human beings- the speech production system- representing speech in time and frequency domain- speech sounds and features.

Lab Programs:

1.  Implement the task that takes in the audio as input and converts it to text.

2. Apply Fourier transform and calculate a frequency spectrum for a signal in the time domain.

Introduction- spectral analysis models- the bank of filters front end processor- linear predictive coding model for speech recognition- vector quantization.

Lab Programs:

3. Implement sampling and quantization techniques for the given speech signals.

4. Explore linear predictive coding model for speech recognition

The acoustic phonetic approach-The pattern recognition approach-The artificial intelligence approach.

Speech detection- distortion measure- Mathematical consideration- Distortion measure – Perceptual consideration- Spectral Distortion Measure- Incorporation of spectral dynamic feature into distortion measure- Time alignment and normalization.

Lab Programs:

1. Demonstrate different pattern in the given speech sign

2. Implement time alignment and normalization techniques

Introduction- Discrete time Markov processes- Extension to hidden Markov Models- Coin - toss models- The urn and ball model- Elements of a Hidden Markov Model- HMM generator of observation- The three basic problems for HMM’s- The Viterbi algorithm- Implementation issues for HMM’s.

Lab Programs:

7. Implement simple hidden Markov Model for a particular application.

8.Apply Viterbi dynamic programming algorithm to find the most likely sequence of hidden states.

Task specific voice control and dialog- Characteristics of speech recognition applications- Methods of handling recognition error- Broad classes of speech recognition applications- Command and control applications- Voice repertory dialer- Automated call-type recognition- Call distribution by voice commands- Directory listing retrieval- Credit card sales validation.

Lab Programs:

9. Demonstrate automatic speech recognition for Call distribution by voice commands.

10. Apply speech recognition system to access telephone directory information from spoken spelled names (Directory listing retrieval).

1. Intelligent Speech Signal Processing, Nilanjan Dey, Academic Press, 2019.

2. Speech Recognition-The Ultimate Step-By-Step Guide, Gerardus Blokdyk, 5STARCooks,2021.

3. Automatic Speech Recognition- A Deep Learning Approach, Dong Yu, Li Deng, Springer-Verlag London, 2015.

[2] Introduction to Digital Speech Processing, Lawrence R. Rabiner, Ronald W. Schafer, Now Publishers, 2015.

ETE-50%

This course aims at developing an understanding about the fundamental concepts in defining and simulating reinforcement learning, identifying, understanding and implementing various reinforcement learning algorithms.

Definition of a stochastic multi-armed bandit, A k-armed Bandit Problem, Definition of regret, Tracking a Nonstationary Problem, Upper-Confidence-Bound Action Selection, KL-UCB, Gradient Bandits. 

Lab: 

1. Implement Multi-Armed Bandit Problem from Scratch in Python 

2. Implement Tracking Nonstationary Bandit Problem

Self-study topics: Basics of probability and linear algebra.

Markov Decision Problem, policy, and value function, Reward models (infinite discounted, total, finite horizon, and average), Episodic & continuing tasks, Bellman's optimality operator, and Value iteration & policy iteration

Lab: Implement Markov Decision Process (MDP) Simulation and Value Iteration

          Develop a program to perform policy evaluation and improvement for a given MDP

The Reinforcement Learning problem, prediction and control problems, Model-based algorithm, Monte Carlo methods for prediction, and Online implementation of Monte Carlo policy evaluation 

  Lab: 

Implement Model-Free Prediction & Control With Monte Carlo (MC).

Extend the reinforcement learning environment to support model-based algorithms for decision-making.

Bootstrapping; TD(0) algorithm; Convergence of Monte Carlo and batch TD(0) algorithms; Model-free  control: Q-learning, Sarsa, Expected Sarsa

Lab: Implement the TD(0) algorithm for temporal-difference learning in a simulated environment, where an agent interacts with states and receives rewards.

Implement Q-learning and SARSA algorithms for action-value estimation and policy improvement.

n-step returns; TD(λ) algorithm; Need for generalization in practice; Linear function approximation and  geometric view; Linear TD(λ). Tile coding; Control with function approximation; Policy search; Policy  gradient methods; Experience replay; Fitted Q Iteration; Case studies.

Lab: Implement the TD(λ) algorithm with eligibility traces for temporal-difference learning in a simulated environment.

Implement policy gradient methods for policy search, such as REINFORCE or actor-critic algorithms.

1.  

   1. Lattimore, T. and C. Szepesvári. “Bandit algorithms,” First Edition, Cambridge University

   2. Press. 2020

   3. Boris Belousov, Hany Abdulsamad, Pascal Klink, Simone Parisi, and Jan Peters “Reinforcement Learning Algorithms: Analysis and Applications,” 

   4. First Edition, Springer 2021

   5. Alexander Zai and Brandon Brown “Deep Reinforcement Learning in Action,” First Edition,Manning Publications 2020

2.Sugiyama, Masashi.“Statistical reinforcement learning: modern machine learning approaches,” First Edition, CRC Press,2015 

ETE-50%

The course is conceptualized to introduce students to both temporal and spatial statistical analysis. It guides students through the use of various tools for the study and interpretation of spatial and temporal data. The objective of the course is to acquaint students with fundamental methods that they might apply to additional study. It will cover the constraints of application as well as the physical interpretation of the data. Through the course, students will be able to analyze environmental data to make better decisions and manage resources more effectively.