Syllabus for
MSc (Computer Science and Applications)
Academic Year  (2024)

1.      Evaluation Pattern: 60% CIA + 40% 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).

Department of Computer Science of CHRIST (Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation’s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.

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 career. The department also moulds the students to be socially responsible and ethically sound.

MSc Computer Science and Applications is a 4-semester programme offered for the professionals working in the software industry or related fields. This program is intended to enhance student’s academic foundations with comprehensive understanding of advance tools and technologies. The objective of the course is to mould students to acquire analytical, creative and problem-solving skills to meet the industry standards. Enlighten students with the research processes and activities to meet future challenges. The progremme offers courses like neural networks, machine learning, IoT, blockchain etc., to meet current trends in industries.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: Identify the need and scope of Interdisciplinary research.

PO3: Enhance research culture and uphold the scientific integrity and objectivity

PO4: Understand the professional, ethical and social responsibilities

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

PO6: Demonstrate disciplinary competency, employability and leadership skills.

Programme Specific Outcome:

PSO2: Project Management and Professional Ethics: Apply standard practices in software project development

PSO3: Conduct Investigation of Complex Computing Problems: Understand, analyze and develop computer-based systems of varying complexity

PSO4: Computer Science Research and Development: Develop computer science research competence for high profile career opportunities

PSO5: Modern Tool Usage: Apply their knowledge and experience on modern computing tools and platforms for continuing professional development

PSO6: Innovation and Entrepreneurship: Produce innovative IT products and services based on global needs and trends

Programme Educational Objective:

To introduce the concepts and principles of Java Programming language and to design and implement object-oriented solutions to simple and complex problems. To experience in java programming within an integrated development environment

Introduction to Java - Class and Objects - Naming conventions and data types - Operators - Control Statements - Input Output - Strings - Methods - Relationships between objects - OOPS concepts - Access Specifiers - Inheritance - Method Overriding and Overloading - Dynamic method dispatch - Polymorphism - Type casting - Abstract classes and Interfaces - Packages - Exception Handling - Wrapper classes

I/O Streams - Readers and Writers - Using various I/O classes – Reader, Writer, Input Stream and Output Stream and File handling - Serialization of objects - Generics - Multithreading - Life cycle of a thread - Java Thread priorities - Runnable interface and Thread Class - Sharing limited Resources - Shared Object with Synchronization - The Collection Framework - Streams API (Java 8) - Lambda expressions - Enumerations and Annotations

Networking - Creating a server that sends data - Creating a client that receives data - two way communications between server and client - Difference between Server Socket and Socket – RMI - JDBC - Using MS-Sql Server Stages in a JDBC program - Registering the driver - Connecting to database - Transaction and Non-Transactional Events - Preparing SQL statements - various methods of statements and differences - Improving the performance of a JDBC program

Java Server Pages - The Problem with Servlets - Life Cycle of JSP Page - JSP Processing - JSP Application Design with MVC - Setting Up the JSP - Environment - JSP Directives - JSP Action - JSP Implicit Objects - JSP Form Processing - JSP Session and Cookies Handling - JSP Session. Tracking - JSP Database Access - JSP Standard Tag Libraries - JSP Custom Tag - JSP Expression Language - JSP Exception Handling - JSP XML Processing

Introduction to Spring framework - Spring beans - Bean Lifecycle - Dependency injection - Maven - Spring Initializr - Spring & Spring Boot Starter Dependency - Spring and Spring Boot Annotations - Spring data JPA and Caching - RESTful microservices - Postman - Spring Security - Logging - Testing using JUNITs

[2].     Kathy walrath,“ Java server programming J2EE”, 1st ed.,Black Book, Dream Tech Publishers, , 2015

[2].     RaoNageswara, “Core Java: An Integrated Approach”, Dreamtech press, 2nd Edition, 2010.

[3].     James Keogh, “Complete Reference J2EE”McGraw publication, 2015.

To enable the students to learn the basic functions, principles and fundamental aspects of computer architecture and design in terms of digital logic elements and circuits, central processing unit and memory unit.

Number system- Decimal number system- Binary number system - octal number system - hexadecimal number system - number system conversion - number representation - unsigned representation – signed number representation - 1’s complement – 2’s complement- 9’s complement – 10’s complement - binary arithmetic operation- binary addition- binary subtraction - Binary multiplication - binary division - Binary codes - weighted codes and unweighted codes

Introduction - Boolean algebra - Boolean operators - truth table - laws of Boolean algebra - De Morgan’s Law - Logic gates - Description of logic gates - Universal properties - Simplification of logic functions - Simplification using NAND and NOR  gate - logic expression - minterm – maxterm - SOP - POS expression - minimization techniques - Karnaugh Map

Self learning: Implementation using simulator

Digital circuits - Combinational circuits - Half Adder – Full adder - Half subtractor - Full subtractor – Encoder – Decoder - BCD to seven segment display – Multiplexer - Demultiplexer

Sequential circuits – Latches – SR-Latch - Flip Flop - SR flip flop - D flip flop - JK flip flop- master slave JK FF - Timing diagrams – Registers - Shift Register - SISO – SIPO – PISO – PIPO – Counters - Synchronous counters - Asynchronous counters - Decade counter - Mod counters- Timing diagrams

Basic Structure of Computers: Basic Operational Concepts - Bus Structures – Processor Clock - Clock Rate - Instruction set: CICS and RISC.

Basic Processing Unit: Some Fundamental Concepts - Multiple Bus Organization - Hard-wired Control - Micro programmed Control.

The memory system: Semiconductor RAM memories - Internal organization of memory chips - static memories – ROM - Cache memories

[2]. V.Carl Hamacher, Zvonko G. Varanesic and Safat G. Zaky, Computer Organisation, 6th edition, Mc Graw-Hill Inc, 2013.

[2] Bartee, Thomas C, Digital Computer Fundamentals, Tata Mc Graw-Hill, 6th edition, 2013.

[3] William Stallings, Computer Architecture and Organization, PHI, Eigth Edition, 2015.

[4] David A. Patterson and John L.Hennessey, Computer Organization and Design, Morgan Kauffman / Elsevier, Fifth edition, 2014.

This course concentrates on introduction, principles, design and implementation of advanced database concepts. Objective of the course is to provide strong foundation of database concepts and develop skills for the design,   storage and retrieval in relational databases, XML and NoSQL databases.

Database system applications - Purpose of database systems - View of data - Data models - Database languages - Database storage and querying - Transaction management - Database architecture - Database users and administrators

Structure of relational databases - Database schema - Keys, Schema diagrams, Design process - ER model – Constraints - ER diagrams - Aspects of database design - atomic domains and 1NF - Decomposition using functional dependencies - Functional dependency theory

File organization - Organization of records in files - Data dictionary storage - Basic indexing concepts - Ordered indexes - B+ tree index - Static hashing - Dynamic hashing - Bitmap index

Motivation - Structure of XML Data - XML Document Schema - Querying and Transformation - Application Program Interfaces to XML - Storage of XML Data - XML Applications.

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

[2].     ShashankTiwari, “Professional NoSQL”, John-Wiley, 2011.

[2].     RamezElmasri, ShamkantNavathe, “Fundamentals of Database Systems”, Addison-Wesley, 6th Edition, 2011.

[3].     Kristina Chodorow, “MongoDB: The Definitive Guide”, O'Reilly, 2nd Edition, 2013.

The objective of this course is to provide comprehensive knowledge of Data Analytics using python program development environment.

Structure of Python Program-Underlying mechanism of Module Execution-Branching and Looping-Problem Solving Using Branches and Loops-Functions - Lists and Mutability- Problem Solving Using Lists and Functions

Sequences, Mapping and Sets- Dictionaries- -Classes: Classes and Instances-Inheritance-Exceptional Handling-Introduction to Regular Expressions using “re” module.

Basics of NumPy-Computation on NumPy-Aggregations-Computation on Arrays-Comparisons, Masks and Boolean Arrays-Fancy Indexing-Sorting Arrays-Structured Data: NumPy’s Structured Array.

Introduction to Pandas Objects - Data indexing and Selection - Operating on Data in Pandas -Handling Missing Data - Hierarchical Indexing - Combining Data Sets - Aggregation and Grouping - Pivot Tables.

Basic functions of matplotlib - Simple Line Plot, Scatter Plot - Density and Contour Plots -Histograms, Binnings and Density - Customizing Plot Legends, Colour Bars - Three-Dimensional Plotting in Matplotlib.

[2] Zhang.Y, An Introduction to Python and Computer Programming, Springer Publications, 2016.

[2] T.R.Padmanabhan, Programming with Python, Springer Publications, 2016.

The course builds the logical thinking in the students with the help of the programming. It also facilitates the students to build applications using java programming. The database concepts help the student to learn advance database connectivity and usage. 

1. 1.  Select queries and DML
2. PL/SQL
3. Data manipulation with MongoDB

1.     Demonstrate objects and classes (constructor, access specifier, method overloading)

2.     Demonstrate static block, static variables and static methods

3.     Demonstrate inheritance in java

4.     Demonstrate use of super and this

5.     Demonstrate abstract class

6.     Demonstrate interfaces in java

7.     Demonstrate exception handling in java

8.     Demonstrate multithreading in java

9.     Demonstrate applets in java

10.  Demonstrate two way communication between server and client

[2].     RaoNageswara, “Core Java: An Integrated Approach”, Dreamtech press, 2nd Edition, 2010.

James Keogh, “Complete Reference J2EE”McGraw publication, 2015.

This course provides a more comprehensive understanding of data structure and algorithm development with practical implantation of the concepts.

Basic Operations – Implementation - Stack applications - Recursion- A Case Study - Queue operations – implementation - Applications of a queue - Linked Lists: Linked list algorithms - Processing a linked list - Linked list algorithms (Create, Traverse, Insert, Delete, Search, Destroy) - doubly linked lists structures and its operations - Applications of linked lists - Algorithmic efficiency in terms of space and timecomplexity

Basic tree concepts - Binary trees - Binary tree traversals - Expression trees - General trees - Changing general tree to binary tree - General tree insertion - Search trees - Binary search trees – Operations – Traversals : BFS and DFS methods - Searching a BST - Algorithms for and traversing and searching - AVL trees - AVL Balance factor - Balancing trees - AVL Insert - AVLDelete

M-Way search trees - B Trees - B-Insertion - B-Tree Deletion - B –Tree Traversal - Tree Search

- Simplified B-Trees - 2-3 Tree - 2-3-4 Tree - B-Tree Variations – B * Trees - B+ Trees.

Terminology – operations - Graph storage structures – Adjacency Matrix - Adjacency lists - Graph algorithms - Create insert vertex - Delete vertex - Retrieve vertex - Depth first traversal and Breadth First Traversal – Networks : Minimum spanning tree - Shortest Pathalgorithm

General sort concepts - O Notation - Sort Algorithms - Quick Sort - Heap sort - Sorting using a Heap - Shell sort - Merge sort - radix sort - merging two sorted lists - Efficiency considerations - comparative study

[2]. A M Tanenbaum, Y Langsam and M. J. Augenstein, “Data Structure using C”, 2nd Edition, Prentice- Hall, India, 2007.

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

This course aims to set the foundation for computer networks and introducing the cryptographic approaches. The course covers the communication process between devices with standard set of protocols based on Internet model (TCP/IP). The last two units presents the cryptographic approaches used in for network security. 

Data Communications- Data Transmission: Concepts and Terminology, Analog and Digital Data Transmission, Transmission Impairments, Channel Capacity; Transmission Media: Guided Transmission Media, Wireless Transmission, Wireless Propagation, Line-of-Sight Transmission; Signal Encoding Techniques: Digital Data, Digital Signals, Digital Data, Analog Signals, Analog Data, Digital Signals, Analog Data, Analog Signals.

Digital Data Communication Techniques- Asynchronous and Synchronous Transmission, Types of Errors, Error Detection, Error Correction, Line Configurations; Multiplexing: Frequency, Division Multiplexing, Synchronous Time-Division Multiplexing, Statistical Time-Division Multiplexing, Asymmetric Digital Subscriber Line, Switched Communications Networks, Circuit Switching Networks, Circuit Switching Concepts, Softswitch Architecture, Packet-Switching Principles.

Congestion Control in Data Networks- Effects of Congestion, Congestion Control, Traffic Management, Congestion Control in Packet-Switching Networks; Cellular Wireless Networks: Principles of Cellular Networks, First Generation Analog, Second Generation CDMA, Third Generation Systems; High-Speed LANs: The Emergence of High-Speed LANs, Ethernet, Fibre Channel; Wireless LANs: IEEE 802.11 Architecture and Services; Internetwork Protocols - Internetwork Protocols: Internet Protocol, IPv6; Transport Protocols: Connection-Oriented Transport Protocol Mechanisms, TCP, TCP Congestion Control, UDP.

Introduction to Cryptography and Data Security - Stream Ciphers -  The Data Encryption Standard (DES) and Alternatives - The Advanced Encryption Standard (AES) - More About Block Ciphers - Introduction to Public-Key Cryptography - The RSA Cryptosystem -  Public-Key Cryptosystems Based on the Discrete Logarithm Problem - Elliptic Curve Cryptosystems

Digital Signatures - The RSA Signature Scheme - The Digital Signature Algorithm (DSA) -  Hash Functions  - Message Authentication Codes (MACs) - Principles of Message Authentication Codes - MACs from Hash Functions: HMAC - Key Establishment.

[2].     Bart Preneel, “Understanding Cryptography”, Springer Heidelberg Dordrecht London New York, 2010.

[2].     AtulKahate, “Cryptography and Network Security”, Tata McGraw-Hills, 2010.

[3].  Brijendra Singh, “Network Security and Management”, PHI, 3rd Edition, 2013.

[4].  William Stallings, “Cryptography and Network Security”, Prentice Hall, 6th Edition, 2014.

ESE (Weightage): 40%

This course starts with an introduction to the basic concepts in research and leads through the various methodologies involved in the research process. It focuses on finding out the research gap from the literature and encourages lateral, strategic, and creative thinking. This course also introduces computer technology and basic statistics required for conducting research and reporting the research outcomes scientifically, with emphasis on research ethics.

Defining research problem - selecting the problem - necessity of defining the problem - techniques involved in defining a problem- Ethics in Research. Kinds of Publication, Writing, Science, and Skepticism, Shaping a Research Project, Research Planning, Students and Advisors, A “Getting Started” Checklist. Reading and Reviewing: Research Literature, Finding Research Papers, Critical Reading, Developing a Literature Review, Authors, Editors, and Referees, Contribution, Evaluation of Papers, Content of Reviews, Drafting a Review.

Hypotheses, Defending Hypotheses, Forms of Evidence, Use of Evidence, Approaches to Measurement. Writing a Paper: The Scope of a Paper, Organization, Title and Author, Abstract, Introduction, Body, Literature Review, Conclusions, From Draft to Submission, Co-authoring, Thesis. Algorithms: Presentation of Algorithms, Formalisms, Environment of Algorithm. Graphs, Figures, and Tables, Editing: Proofreading, Choice of Word-Processor.

Latex: Introduction, text, tables, figures, equations, citations, referencing, and templates (IEEE style), paper writing for international journals, Writing scientific report: literature review. On-line Searching: Database – SciFinder – Scopus - Science Direct - Searching research articles - Citation Index - Impact Factor - H-index etc.

Persuasive Data, Interpretation, Robustness, Performance of Algorithms, Human Studies, Coding for Experimentation, Describing Experiments.  Statistical Principles: Variables, Samples and Populations, Aggregation and Variability, Reporting of Variability, Statistical Tools, Randomness and Error, Intuition, Visualization of Results

Research Talks, Content, Organization, The Introduction, The Conclusion, Preparation, Delivery, Slides, Copyright, Posters. Ethics: Intellectual Creations, Plagiarism, Self-plagiarism, Misrepresentation, Authorship, Confidentiality and Conflict of Interest

[2] Zina O’Leary, The Essential Guide of Doing Research, 3rd Edition, SAGE Publications Ltd, 2017.

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 4th Edition, SAGE Publications Ltd, 2014.

This course aims at developing an understanding about the issues involved in defining and simulating perception, identifying the problems where AI is required and the different methods available, to compare and contrast different AI techniques available, to define and explain learning algorithms and to provide the student additional experience in the analysis and evaluation of complicated systems.

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 

Searching: Uninformed search strategies – Breadth first search, depth first search. Generate and Test, Hill climbing, simulated annealing search, Constraint satisfaction problems, 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.

Overview, Minimax algorithm, Alpha-Beta pruning, Additional Refinements. Classical planning problem, STRIPS- basic process and working of system – Planning and Acting in the Real World

Introduction, Syntax processing, Semantic Analysis, Pragmatic and DisCourse Description: Analysis - Perception.

[2] S. Russell and P. Norvig, Artificial Intelligence A Modern Approach, 3^rd  Edition,  

     Pearson Education, 2019.

[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.

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

This course provides different ways to solve business intelligence problems using various data structures and writing programs for these solutions

1. Implementation of insertion, selection and merge sorting Methods

2. Implementation of stacks

3. Implementation of queues

4. Implementation of Linked list

5. Implementation of two way linked list

6. Implementation of circular linked List

7. Implementation of Binary search tree

8. Implementation of radix and heap sort

9. Implementation of DFS for graphs

10. Implementation of BFS for graphs

Pre-Lab: Software Installation and Configuration

1. Create a Relational Data bases

2. Design an ETL System using SSIS

3. Design a dimensional model using SSAS

4. Design reports using SSRS

5. Design a dashboard using power pivot

2. Joy Mundy, Warren Thornthwaite and Ralph Kimball, “The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset”, John Wiley & Sons, 2nd edition, 2011.

2.Gert H. N. Laursen and Jesper Thorlund, “Business Analytics for Managers: Taking Business Intelligence beyond Reporting Paperback” , 2013

This course is designed to introduce the students to advanced technologies which can help realization of complex Web applications. Students examine advanced topics in Hyper Text Markup Language, Cascade Style Sheet and JavaScript for interactive web applications that use rich user interfaces and also understand the server-side web technologies for dynamic web applications and creating modern web applications using MEAN and FULL Stack.

Internet and web Technologies- Client/Server model -Web Search Engine-Web Crawling-Web Indexing-Search Engine Optimization and Limitations-Web Services –Collective Intelligence –Mobile Web –Features of Web 3.0-HTML vs HTML5-Exploring Editors and Browsers Supported by HTML5-New Elements-HTML5 Semantics-Canvas-HTML Media - Introduction to CSS3-CSS2 vs CSS3

XML-Documents and Vocabularies-Versions and Declaration -Namespaces JavaScript and XML: Ajax-DOM based XML processing Event-Transforming XML Documents-Selecting XML Data:XPATH-Template based Transformations: XSLT-Displaying XML Documents in Browsers - Evolution of AJAX -Web applications with AJAX -AJAX Framework

JavaScript Implementation - Use Javascript to interact with some of the new HTML5 apis -Create and modify Javascript objects- JS Forms - Events and Event handling-JS Navigator-JS Cookies-Introduction to JSON-JSON vs XML-JSON Objects-Importance of Angular JS in web-Angular Expression and Directives-Single Page Application

Introduction to Node.js-REPL Terminal-Package Manager (NPM)-Node.js Modules and filesystem-Node.js Events-Debugging Node JS Application-File System and streams-Testing Node JS with jasmine - Express JS

Introduction to MySQL- Performing basic database operation (DML) (Insert, Delete, Update, Select)-Prepared Statement- Uploading Image or File to MySQL- Retrieve Image or File from MySQL - CRUD operation using MongoDB

2. HTML 5 Black Book (Covers CSS3, JavaScript, XML, XHTML, AJAX, PHP, jQuery), DT Editorial Services, Dreamtech Press,Second Edition,2016

2. Mastering HTML, CSS & Javascript Web Publishing, Laura Lemay, Rafe Colburn & Jennifer Kyrnin, BPB Publications, First edition,2016

3. Mastering MongoDB 3.x, Alex Giamas Packt Publishing Limited,First Edition,2017

The course will expose few advanced topics in operating system and concepts related to recent developments in operating system. Objectives of the course are to understand the main concepts of parallel processing systems, distributed systems, real time systems etc., to have an insight into UNIX and MACH operating system and to know the components and management aspects of Real time, Mobile Operating Systems.

General Overview of the System - System Structure – Operating System Services – Introduction to kernel - architecture of unix operating system - introduction to system concepts kernel data structures - The Buffer cache - Buffer Headers – Structure of the buffer pool – Retrieval of a buffer – scenarios for retrieval of a Buffer - Reading and writing disk blocks – Advantages and disadvantages of the buffer cache - Internal Representation of files – Inodes - structure of a regular file - directories - conversion of a path to an inode - Super Block - inode assignment to a New File - Allocation of Disk Blocks - Other file types

UNIX Process Management - The Structure of Processes: Process States and Transitions - Layout of system memory - Context of a process – Sleep – Implementation of System Calls. Process Control - Process Creation – Signals – Process Termination – Invoking other programs – PID & PPID – Changing the size of a process – The shell – System Boot and the init process

Memory Management: Swapping – Demand Paging – A Hybrid System with Swapping and Demand Paging. The I/O Subsystem: Driver Interfaces – Disk Drivers – Terminal Drivers – Streams. Inter Process Communication (IPC): Process Tracing – System V IPC – Network Communications – Sockets. Multiprocessor Systems: Problem with Multiprocessor Systems – Master and Slave processors – Semaphores

Introduction to Distributed system – Remote Procedure Call – Logical clocks – Vector clocks – Distributed mutual exclusion – Non token based algorithms – Token based algorithms – Deadlock algorithms – Election algorithms – Byzantine agreement problem – Load distributing algorithms – Performance comparison. Distributed File system Design – an overview

Real time and Mobile Operating Systems – Basic Model of Real Time Systems – Characteristics – Applications of Real Time Systems – Real time Task Scheduling –Handling resource sharing . Mobile Operating System – Micro Kernel Design – Case study MACH: Introduction to MACH - Process management in MACH-processes-thread scheduling – memory management in MACH-Virtual memory – memory sharing

[2].     Andrew S Tanenbaum, “Distributed Operating Systems”, PHI, reprint 2006.

Rajib Mall, “Real Time Systems: Theory and Practice”, Pearson Education, India, 2006.

[2].     Arnold Robbins, “UNIX in a Nutshell”, In a Nutshell series, 3rd Edition, reprint 2007.

[3].     George Coulouris, Jean Dollimore and Tim Indberg, “Distributed Systems Concepts and Design”, 3rd Edition, Pearson Education, 2002.

Pradeep K Sinha, “Distributed Operating Systems – Concepts and Design”, PHI, 2006.

The goal is to make students familiar with the basic concepts and structure of modern wireless and mobile communication networks.

Wireless Telecommunications Systems and Networks: History and Evolution of Wireless Radio Systems - Development of Modern Telecommunications Infrastructure - Overview of Existing Network Infrastructure - Wireless Network Applications: Wireless Markets

Evolution and Deployment of Cellular Telephone Systems: Different Generations of Wireless Cellular Networks - 1G Cellular Systems - 2G Cellular Systems - 2.5G Cellular Systems - 3G Cellular Systems - 4G Cellular Systems and Beyond - Wireless Standards Organizations

Common Cellular System Components: Common Cellular Network Components - Hardware and Software Views of the Cellular Network - 3G Cellular System Components - Cellular Component Identification - Cell establishment

Wireless Network Architecture and Operation: The Cellular Concept - Cell Fundamentals - Capacity Expansion Techniques - Mobility Management - Wireless Network Security

Introduction to GSM and TDMA - GSM Network and System Architecture - GSM Channel Concept - GSM Identities - GSM System Operations - GSM Infrastructure Communications

CDMA Technology: Introduction to CDMA - CDMA Network and System Architecture - CDMA Channel Concept - CDMA System Operations

CDPD and Edge Data Networks: CDPD – GPRS - GPRS Networks - GPRS Network Details - GPRS Network Layout and Operation - GPRS Packet Data Transfer - GPRS Protocol Reference Model - GPRS Logical Channels - GPRS Physical Channels - GSM/GPRS/Edge Technology

Introduction to wireless LAN 802.11X technologies - Evolution of Wireless LAN - Introduction to IEEE 802.15x Technologies - Wireless PAN Applications and Architecture – Bluetooth -  Introduction to Broadband wireless MAN - 802.16 technologies

[2].     Stallings William, “Wireless Communications and Networks”, Pearson Education Asia, 2nd Edition, 2009.

[3].     Theodore S Rappaport, “Wireless Communications: Principles and Practice”, Pearson Education Asia, 2nd Edition, 2009.

[4].     Jochen Schiller, “Mobile Communication”, Addison-Wesley, 2nd Edition, 2011.

The explosive growth of the “Internet of Things” is changing our world and the rapid growth of IoT components is allowing people to innovate new designs and products at home.  Wireless Sensor Networks form the basis of the Internet of Things. To latch on to the applications in the field of IoT of the recent times, this course provides a deeper understanding of the underlying concepts of IoT and Wireless Sensor Networks.

Introduction to IoT - Definition and Characteristics, Physical Design Things- Protocols, Logical Design- Functional Blocks, Communication Models- Communication APIs- Introduction to measure the physical quantities, IoT Enabling Technologies - Wireless Sensor Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded System- IoT Levels and Deployment Templates.

Introduction to Smart Systems using IoT - IoT Design Methodology- IoT Boards (Rasberry Pi, Arduino) and IDE - Case Study: Weather Monitoring- Logical Design using Python, Data types & Data Structures- Control Flow, Functions- Modules- Packages, File Handling - Date/Time Operations, Classes- Python Packages of Interest for IoT.

Home Automation – Smart Cities- Environment, Energy- Retail, Logistics- Agriculture, Industry- Health and Lifestyle- IoT and M2M.

Sensing and Sensors, Wireless Sensor Networks, Challenges and Constraints; Applications:  Structural Health Monitoring, Traffic Control, Health Care; Node Architecture, Operating system

Introduction – Fundamentals of MAC Protocols – MAC protocols for WSN – Sensor MAC Case Study – Routing Challenges and Design Issues – Routing Strategies – Transport Control Protocols – Transport Protocol Design Issues – Performance of Transport Protocols

[2].     KazemSohraby, Daniel Minoli, TaiebZnati ,“Wireless Sensor Networks: Technology. Protocols and Application”, Wiley Publications, 2010 (Unit IV & V)

[3].     WaltenegusDargie, Christian Poellabauer, "Fundamentals of Wireless Sensor Networks: Theory and Practice", A John Wiley and Sons, Ltd., Publication, 2010.

[2].     Michael Miller, “The Internet of Things” , Pearson Education, 2015

[3].     Holger Karl, Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, John Wiley & Sons, Inc., 2005.

[4].     ErdalÇayırcı ,ChunmingRong, “Security in Wireless Ad Hoc and Sensor Networks”, John Wiley and Sons, 2009.

[5].     Carlos De MoraisCordeiro, Dharma PrakashAgrawal, “Ad Hoc and Sensor Networks: Theory and Applications (2nd Edition)”, World Scientific Publishing, 2011.

[6].     WaltenegusDargie, Christian Poellabauer, “Fundamentals of Wireless Sensor Networks Theory and Practice”, John Wiley and Sons, 2010

[7].     Adrian Perrig, J. D. Tygar, "Secure Broadcast Communication: In Wired and Wireless Networks", Springer, 2006.

Understand the concept of high performance computing and its applications. The course includes the introduction to load sharing and balancing concepts. The course will discuss Grid computing and cloud computing in terms of their architecture and performance.

Introduction to Cluster Computing, Scalable Parallel Computer Architectures, Cluster Computer and its Architecture, Classifications, Components for Clusters, Cluster Middleware and Single System Image, Resource Management and Scheduling

Programming Environments and Tools, Applications, Representative Cluster Systems, Heterogeneous Clusters, Security, Resource Sharing, Locality, Dependability, Cluster Architectures, Detecting and Masking Faults, Recovering from Faults, Condor, Evolution of Metacomputing.

Load Sharing and Balancing: Evolution, Job and Resource Management Systems, State-of-the-Art in RMS and Job, Rigid Jobs with Process Migration, Communication-Based Scheduling, Batch Scheduling, Fault Tolerance, Scheduling Problem for Network Computing, Dynamic Load Balancing, Mapping and Scheduling, Task Granularity and Partitioning, Static and Dynamic Scheduling.

Grid Computing: Introduction to Grid Computing, Virtual Organizations, Architecture, Applications, Computational, Data, Desktop and Enterprise Grids, Data-intensive Applications, High-Performance Commodity Computing, High-Performance Schedulers, 

 Consistent, Inconsistent and Partially-Consistent, QoS Guided Min-Min, Selective Algorithm, Grid Computing Security.

Immediate and Batch Mode

Connectivity, Resource and Collective Layer, Heterogeneous Computing Systems

Task Computing and Task-based Application Models, Market-Based Management of Clouds, Energy-Efficient and Green Cloud Computing Architecture, Resource Allocation, Leases, 

RR, CLS and CMMS, Workflow Scheduling, Montage, Epigenomics, SIPHT, LIGO, CyberShake, Task Consolidation, Introduction to CloudSim, Cloudlet, VirtualMachine and its Provisioning, Time and Space-shared Provisioning.

Introduction to Cloud Computing, Types: Deployment and Service Models, Characteristics, Applications, Service-Level Agreement, Virtualization, 

[2].      (Edited By) I. Foster and C. Kesselman, “The Grid: Blueprint for a New Computing Infrastructure”, Morgan Kaufmann, Elsevier, 2004.

[3].      D. Janakiram, “Grid Computing”, Tata McGraw-Hill, 2005.

[4].      R. Buyya, C. Vecchiola and S. T. Selvi, “Mastering Cloud Computing Foundations and Applications Programming”, Morgan Kaufmann, Elsevier, 2013.

[2].      B. Wilkinson, “Grid Computing: Techniques and Applications”, CRC Press, 2009.

[3].      C. S. R. Prabhu, “Grid and Cluster Computing”, PHI, 2008.

[4].      B. Sosinsky, “Cloud Computing Bible”, Wiley, 2011.

ESE (Weightage): 40%

To present the main characteristics of distributed systems, as well as the related problems and the most common solutions. Student can implement a simple distributed application using a message based protocol.

Distributed System, Examples of Distributed Systems, Important Issues in Distributed Systems, Implementing a Distributed System, Parallel versus Distributed Systems.

Inter process Communication: Introduction, Processes and Threads, Client–Server Model, Middleware, Network Protocols, Ethernet, Wireless Networks, OSI Model, IP, Transport Layer Protocols, Inter process Communication Using Sockets

The Client-Server Model – (Client and Servers, Addressing, Block versus Nonblocking Primitives, Buffered versus Unreliable Primitives) – Remote Procedure Call – (Basic RPC Operation, Parameter Passing, Dynamic Binding, RPC Semantics in the Presence of Failures) Distributed objects and remote invocation. Models for Communication: Need for a Model, Message-Passing Model for Inter process Communication, Process Actions, and Synchronous versus Asynchronous Systems.

Introduction -- Clock Synchronization – (Logical Clocks, Physical Clocks, Clock Synchronization Algorithms) – Election Algorithms – (The Bully Algorithm, A Ring Algorithm) – Atomic Transactions – (Introduction to Atomic Transactions, The Transaction Model. Implementation, Concurrency Control) – Deadlocks in Distributed Systems – (Distributed Deadlock Detection & Prevention).Resource Deadlock and Communication Deadlock, Detection of Resource Deadlock, Detection of Communication Deadlock

Introduction, Solutions on Message-Passing Systems, Lamport’s Solution, Ricart–Agrawala’s Solution, Maekawa’s Solution, Token-Passing Algorithms, Suzuki–Kasami Algorithm, Raymond’s Algorithm’ - Distributed Snapshot: Introduction, Properties of Consistent Snapshots, Cuts and Consistent Cuts, Chandy–Lamport Algorithm.Global State Collection: Introduction, Elementary Algorithm for All-to-All Broadcasting, Termination-Detection Algorithms, Dijkstra–Scholten Algorithm.

Fault-Tolerant Systems: Introduction, Classification of Faults, Specification of Faults, Fault-Tolerant Systems, Masking Tolerance, Non-masking Tolerance, Fail-Safe Tolerance, Graceful Degradation, Detection of Failures in Synchronous Systems, Tolerating Crash Failures. Distributed File Systems: Introduction – Distributed File System Design – (The File Service Interface, The Directory Server Interface, Semantics of File Sharing) -- Distributed File System Implementation – (File Usage, System Structure, Caching, Replication, An Example: Sun’s Network File System).Distributed Shared Memory: Introduction, What is Shared Memory, Consistency Models, Page-Based Distributed Shared Memory.

[2].     Coulouris G., Dollimore J., Kindberg T. and Blair G., “Distributed Systems: Concepts and Design, Addison-Wesley”, 5th Edition, 2011

[2].     SinghalMukesh, Shivaratri G Niranjan, “Advanced Concepts In Operating Systems Distributed Data Base, And Multiprocessor Operating Systems”, McGraw-Hill, Inc., 2009.

This course provides a broad and in-depth knowledge of Storage and Storage networking concepts, applications, and technologies. Storage Fundamentals including storage attachment architectures, the SCSI protocol, disk and tape drive concepts, RAID and JBOD, IP-based SANs, and Storage Networking Issues. 

Management, Storage System Environment: Information Storage, Evolution of Storage Technology and Architecture, Data Center Infrastructure, Key Challenges in Managing Information, Information Lifecycle Components of Storage System Environment, Disk Drive Components, Disk Drive Performance, Fundamental Laws Governing Disk Performance, Logical Components of the Host, Application Requirements and Disk Performance.

Data Protection, Intelligent Storage system: Implementation of RAID, RAID Array Components, RAID Levels, RAID Comparison, RAID, Impact on Disk Performance, Hot Spares Components of an Intelligent Storage System, Intelligent Storage Array

Types of DAS, DAS Benefits and Limitations, Disk Drive Interfaces, Introduction to Parallel SCSI, Overview of Fibre Channel, The SAN and Its Evolution, Components of SAN, FC Connectivity, Fibre Channel Ports, Fibre Channel Architecture, Zoning, Fibre Channel Login  Types, FC Topologies

General – Purpose Service vs. NAS Devices, Benefits of NAS, NAS File I / O, Components ofNAS, NAS Implementations, NAS File-Sharing Protocols, NAS I/O Operations, FactorsAffecting NAS Performance and Availability.iSCSI, FCIP.Content-Addressed Storage, Storage Virtualization: Fixed Content and Archives, Types of Archive, Features and Benefits of CAS, CASArchitecture, Object Storage and Retrieval in CAS, CAS Examples. Forms of Virtualization,SNIA Storage Virtualization Taxonomy, Storage Virtualizations Configurations, StorageVirtualization Challenges, Types of Storage Virtualization.

Information Availability, BC Terminology, BC Planning Lifecycle, Failure Analysis, BusinessImpact Analysis, BC Technology Solutions. Backup Purpose, Backup Considerations, BackupGranularity, Recovery Considerations, Backup Methods, Backup Process, Backup and restoreOperations, Backup Topologies, Backup in NAS Environments, Backup Technologies.Securing the Storage Infrastructure, Managing the Storage Infrastructure: Storage Security Framework, Risk Triad, Storage Security Domains, Security Implementationsin Storage Networking Monitoring the Storage Infrastructure, Storage Management Activities,Storage Infrastructure Management Challenges, Developing an Ideal Solution.

[2].     Rebert Spalding,“Storage Networks”, The Complete Reference, Tata McGraw Hill,2003.

[3].     Richard Barker and Paul Massiglia: Storage Area Networks Essentials A CompleteGuide to Understanding and Implementing SANs, Wiley India, 2002.

Blockchain is an emerging technology platform for developing decentralized applications and data storage, over and beyond its role as the technology underlying the cryptocurrencies. This course serves as an introduction to the exciting new world of blockchain technologies. It explains in detail how bitcoins are generated/mined, how transactions are being made and how the blockchain makes it possible to keep everything secure, fast and reliable without depending in any trusted party or a financial institution. Limitations of Bitcoin and new cryptocurrency systems will be explored.

Blockchain evolution – Blockchain structure – Blockchain characteristics – Blockchain applications example: Escrow – Blockchain Stack – Domain specific blockchain applications.

Cryptographic hash functions – Hash pointer and data structures – Digital signatures – Public keys as identities – A simple cryptocurrency – Distributed consensus – Consensus without identity using a block chain.

Bitcoin transactions – Bitcoin scripts – Applications of Bitcoin scripts – Bitcoin blocks – Bitcoin network – Simple local storage – Hot and cold storage – Splitting and sharing keys – Online wallets and exchanges – Payment services – Transaction fees – Currency exchange markets.

Task of bitcoin miners – Mining hardware – Mining pools – Mining incentives and strategies – Bitcoin anonymity – Deanonymizing bitcoin – Mixing – Zerocoin and zerocash

History and motivation – Few altcoins in detail – Relationship between bitcoin and altcoins – Merge mining – Atomic cross-chain swaps – Bitcoin backed altcoins – Ethereum and smart contracts.

2. Bitcoin and cryptocurrency technologies: a comprehensive introduction. Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, and Steven Goldfeder. Princeton University Press, First edition, 2016.

2. Blockchain: Blueprint for the new economy. Melanie Swan. O’Reilly. First edition. 2015.

To acquire basic knowledge in machine learning techniques and learn to apply the techniques in the area of pattern recognition and data analytics

Machine Learning - types of machine learning examples - Supervised Learning: Learning class from examples - VC dimension - PAC learning – noise - learning multiple classes – regression - model selection and generalization - dimensions of a supervised learning algorithm - Parametric Methods: Introduction - maximum likelihood estimation - evaluating estimator - Bayes’ estimator - parametric classification

Introduction - subset selection - principal component analysis - factor analysis - multidimensional scaling - linear discriminant analysis - Clustering: Introduction - mixture densities - k-means clustering - expectation-maximization algorithm - hierarchical clustering - choosing the number of clusters - Non-parametric: Introduction - non-parametric density estimation - non-parametric classification

Introduction, univariate trees, pruning, rule extraction from trees, learning rules from data. Multilayer perceptron: Introduction, training a perceptron, learning Boolean functions, multilayer perceptron, backpropogation algorithm, training procedures

Introduction, optical separating hyperplane, v-SVM, kernel tricks, vertical kernel, defining kernel, multiclass kernel machines, one-class kernel machines. Bayesian Estimation: Introduction, estimating the parameter of a distribution, Bayesian estimation, Gaussian processes. Hidden Markov Models: Introduction, discrete Markov processes, hidden Markov models, basic problems of HMM, evaluation problem, finding the state sequence, learning model parameters, continuous observations, HMM with inputs, model selection with HMM.

Introduction, canonical cases for conditional independence, d-separation, Belief propagation, undirected graph: Markov random field. Reinforcement Learning: Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partially observed state.

 Self Learning: Clustering - Decision tree

Service Learning: Introduction to machine learning applications developed for betterment of society through select case studies.

[2].     P. Harrington, “Machine Learning in Action”, Manning Publications, 2012

[3].     C. M. Bishop, “Pattern Recognition and Machine Learning”,  Springer, 2011.

[4].     S. Marsland, “Machine Learning: An Algorithmic Perspective”, 1st Ed. Chapman and Hall, 2009.

[5].     T. Mitchell, “Machine Learning”, McGraw-Hill, 1997

Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications support the activities of decision support, query and reporting, online analytical processing (OLAP) and analysis.

Defining Business Requirements: Introduction, Uncovering Business Value, Prioritizing the Business Requirements. Designing the Business Process Dimensional Model: Concepts and Terminology, Additional Design Concepts and Techniques. The Toolset: Microsoft DW/BI Toolset, Architecture and Overview of the Toolset.

Creating the Relational Data Warehouse: Getting started, completing the physical design, Define storage and create constraints and supporting objects.

Master Data Services: Managing Master Reference Data, Introducing SQL Server MDS, Creating a Simple Application.

Design and Develop the ETL System: Developing the ETL Plan, Introducing SQL Server Integration Services, Extracting Data, Cleaning and Confirming Data, Delivering Data for Presentation.

Core Analysis Services OLAP Database: Overview, Design the OLAP structure-Planning, getting started, Data source view, Dimension design, Editing dimension, Editing Cube, Physical Design Consideration.

Building the BI Applications in Reporting Services: Overview, High Level Architecture for Reporting, System Design and Development Process, Building and Delivering Reports, Reporting Options.

Power Pivot and Excel: Using Excel for Analysis and Reporting, Architecture, Creating and using Power Pivot Databases, Power pivot Monitoring and Management.

Case study: Any Two Applications (eg. Healthcare, Retail Industry)

[2]. Mike Biere,“Business Intelligence for the Enterprise” , second edition, 2009

To provide fundamental concepts of risk analysis and relevant details related to types of risks, risk management strategies and tools used for risk analysis.

Definition -Understanding Risk- Nature of Risk, Source of Risk, Need for risk, management, Benefits of Risk Management, Risk Management approaches.

Credit risk, market risk, operational risk and other risk, Risk Measurements -Measurement of Risk – credit risk measurement, market risk, measurement, interest rate risk measurement, Asset liability management, measurement of operational risk

Risk management- Managing credit risk, managing operational risk, managing market risk, insurance

Derivatives, combinations of derivative instruments, Neutral and volatile strategies, credit derivatives, credit ratings, swaps.

Issues in risk management – Regulatory framework, Basel committee, legal issues, accounting issues, tax issues, MIS and reporting, integrated risk management

[2]. AswathDamodharan,“Strategic Risk Taking”, Pearson, 2008.

The main objective of the courseis aimed at an entry level study of information retrieval and web mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and web mining are covered, the Course Description: is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations.

Introduction to Data mining. Relationship to machine learning. Summarization and feature extraction.Data Preprocessing: Introduction to  preprocessing. Data summarization. Date cleaning. Data integration, Data transformation. Data cube aggregation, attribute subset selection, Dimensionality reduction, Numerosity reduction. Data Discretization, Concept Hierarchy generation.

Inverted indices and Boolean queries. Query optimization. The nature of unstructured and semi-structured text.The term vocabulary and posting lists. Text encoding: tokenization, stemming, lemmatization, stop words, phrases. Optimizing indices with skip lists. Proximity and phrase queries.Positionalindices.Dictionaries and tolerant retrieval.Dictionary data structures. Wild-card queries, permuterm indices, n-gram indices. Spelling correction and synonyms: edit distance, soundex, language detection.

Postings size estimation, sort-based indexing, dynamic indexing, positional indexes, n-gram indexes, distributed indexing

Term weighting, and the vector space model. Parametric or fielded search.Documentzones.The vector space retrieval model.tf.idf weighting. The cosienmeasure.Scoringdocuments.  Map Reduce: Distributed file systems, Map and reduce tasks. Algorithms that use map-reduce: Matrix vector multiplication, Relational algebra operations. Mining Frequent Patterns and Associations: Near-neighbor search, Collaborative filtering, Shingling. Min-hashing and locality  sensitive hashing.

The stream data model, examples of stream sources and queries, sampling data in a stream. Filtering streams, bloom filters, counting distinct elements in a stream. Market-Basket model, Association rules. A-priori algorithm.Classification: Introduction to text classification. Naïve Baye’s models. Spam filtering. K nearest neighbors, Decision boundaries, vector space classification using centroids.Comparative results. Support vector machine classifiers. Kernel function.Evaluation of classification.Micro-and macro-averaging.Learning rankings.

Introduction to the problem.Partitioning methods: K-means clustering; Hierarchical clustering.Latent semantic indexing (LSI).Applications to clustering and to information retrieval.Web Mining: Introduction to  web . Web search overview, web structure, the user, paid placement, search engine optimization/spam. Web measurement.Crawling and web indexes.Near-duplicate detection.Linkanalysis.Web as a graph.PageRank.Machine learning techniques for ranking.

[2].      AnandRajaraman and Jeffery D.ullman, “Mining the Massive”,Cambridge University Press, 2008.

[2].      K.P Soman, Shyamdiwakar and VAjay, “Insight into Data Mining – Theory and Practice”, 6th Ed  print, PHI India, 2012.

[3].      Jiawei Han and MichelineKamber, “Data Mining: Concepts and Techniques”, 2nd Edition,      2006, Morgan Kaufmann Publishers, San Francisco, USA.

The main objective of the course is designed to introduce the core concepts of data mining and data warehousing techniques and implementation

Basic elements of the Data Warehouse: Source system-Data staging Area-Presentation Server-Dimensional Model-Business process-Data Mart-Data warehouse-Operational Data Store-OLAP: ROLAP, MOLAP and HOLAP. DataWarehouse Design:The case for dimensional modeling – Putting Dimensional modeling together: the data warehouse bus architecture – Basic dimensional modeling techniques.

The value of architecture – An architectural framework and approach – Technical architecture overview – Back room data stores – Back room services. Back Room Services. Data Staging:Data staging overview – Plan effectively – Dimension Table staging – Fact Table loads and warehouse operations – Data quality and cleansing – issues.

Data Mining – Process and architecture - Kinds of Data to be mined - Data Mining Functionalities, Classification of Data Mining Systems, Data Mining Task Primitives, Major Issues in Data Mining. Data Preprocessing:Preprocessing - Descriptive Data Summarization – Measuring the central tendency- Measuring the dispersion of data - Data Cleaning - Missing Values – Noisy Data - Data Cleaning as a Process - Data Integration and Transformation - Data Reduction-Data Cube Aggregation-Attribute Subset Selection. Demo: Preprocessing can be done using WEKA tool.

Association Rule Mining: Basic Concepts, Efficient and Scalable Frequent Item set Mining Methods – Apriori algorithm, Generating Rules – Improving efficiency – Mining frequent item set without candidate generation. Classification and Prediction: Issues Regarding Classification and Prediction, Accuracy and Error Measures.Cluster Analysis:Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods – K-Means and K-Medoids, Hierarchical Methods  Agglomerative and Divisive

Demo: Classification and clustering analysis can be done using WEKA tool.

Mining Time-Series Data – Trend analysis – Similarity search, Spatial Data Mining-Spatial Data Cube Construction and Spatial OLAP- Mining Spatial Association and Co-location Patterns-Spatial Clustering, Classification Methods-Mining Raster Databases. Applications and Trends in Data Mining: Data Mining Applications, Data Mining System Products and Research Prototypes, Social Impacts of Data Mining.

[2].    Jiawei Han and MichelineKamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, San Francisco, USA, 2nd Edition, 2011.

[2].      Margaret H. Dunham, “Data mining-Introductory and Advanced topics”, Pearson       Education, 2003.

[3].      Witten and E. Frank, “Data Mining : Practical Machine Learning Tools and        Techniques”, Morgan Kaufmann Publishers, 2005.

[4].      K P Soman, Shyam Diwakar, V. Ajay,“Insight into Data Mining-Theory and  Practice”, 6thReprint, PHI, 2012.

ESE:  40%

This course provides insight on the administrative tasks, their implementation and effective usage of tools.

Introduction: General Definition of DBA and Security, System Management & Database Design Roles of DBA – DBA Job Classification. Types of Databases: Online Transaction Processing System and Decision Support System Databases, Development, Test & Production Databases. Daily Routine of a DBA.Architecture: Database Structures- Logical & Physical, Trace Files, Data Files &Tablespaces, Oracle Managed Files. Processes- Interaction between User & Oracle Processes, The Server Process, Background Processes. Memory Structures- SGA, PGA. Oracle Transactions- Anatomy of SQL Transactions. Data Consistency & Concurrency- Database Writer & Write Ahead Protocol, The System Change Number, Undo Management. Backup and Recovery Architecture-User managed, RMAN, Flashback Techniques. Data Dictionary and Dynamic Performance Views- Data Dictionary, V$ views.

Installing Oracle 11g: Following OFA, System and Owners Pre-Installation Tasks, Installing Software, System Administrator and Oracle Owner’s Post-Installation Tasks, Uninstalling Oracle 11g.Database Creation: Creating SPFILE and pfile, Initialization Parameters, Creating a new Database, Using SPFILE, Starting up and Shutting Down Database

Database Connectivity and Networking: Working of Oracle Network – instance names, global database names, connect descriptors, identifiers and strings, Establishing Connectivity, Oracle Client, Installing the Client, Naming and Connectivity – Local, Easy connect, External and Directory naming methods. 

Managing Users: Creating, altering and dropping users, Creating user Profiles & Resources, Database Resource Manager, Controlling Access to Data – Roles, Privileges and using Views, Stored Procedures to Manage Privileges, Auditing Database – Standard Auditing, Authentication – Database, External, Centralized user and Proxy Authentication. Database Security Do’s & Don’ts-User Accounts, Passwords, OS authentication, Auditing Database, Granting Appropriate Privileges, Permissions, Application Security.

Loading and Transforming Data: Overview of extraction loading and Transformation, Loading Data-Using the SQL Loader Utility, Using External Tables to Load Data. Overview of Common Techniques used for Transforming Data.Data Pump Technology: Introduction, Benefits, Uses and Components of Data Pump.Access method, Data Pump Files, Privileges, Mechanics of Data Pump Job. 

Backing Up Oracle Databases: Backup Terms, Guidelines, Strategies, Examining Flash Recovery Area – benefits of Flash recovery Area, Looking into Flash Recovery Area, Setting size of Flash Recovery Area Creating Flash Recovery Area, Backing up Flash Recovery Area, RMAN – Benefits, Architecture, Connecting to RMAN. SQL Query Optimization:Approach to Performance Tuning, Optimizing Oracle Query Processing, Cost-based Optimizer, Drawbacks of CBO. SQL Performance Tuning Tools – EXPLAIN PLAN, Auto trace, SQL Trace and TKPROF. Tuning the instance:  Introduction, Automatic Tuning vs. Dynamic Views. Tuning Oracle Memory:

Self Learning:

Tuning Shared Pool – Library Cache, Dictionary Cache, Hard vs. Soft Parsing, Sizing Shared Pool, Tuning Buffer Cache – Sizing buffer Cache, Multiple pools for Buffer Cache.

[2].      Kyte and Thomas, “Expert Oracle”, Oracle Press Publication, Signature Edition, 2005.

CIA 60% 

The course is designed to provide a real-world project development and deployment environment for the students.

*The Web Stack Development practical implementation will be executed in the Specialization Lab

List of Programs                                                                                                      

1.   Develop static pages for a given scenario using HTML

2.   Creating Web Animation with audio using HTML5 & CSS3

3.   Demonstrate Geolocation and Canvas using HTML5

4.   Write an XML file and validate the file using XSD

5.   Demonstrate XSL with XSD

6.   Demonstrate DOM parser

7.   Write a JavaScript program to demonstrate Form Validation and Event Handling

8.  Create a web application using AngularJS with Forms.

9.   Implement a single page web application using AngularJS.

10.  CRUD Operation using AngularJS

11.  Implement web application using AJAX with JSON

12.  Demonstrate to fetch the information from an XML file with AJAX

13.  Demonstrate Node.js file system module

14.  Demonstrate Node.js events 

15.  Implement Mysql with Node.JS

16.  Implement CRUD Operation using MongoDB

Cloud computing has become a great solution for providing a flexible, on-demand, and dynamically scalable computing infrastructure for many applications. Cloud computing presents a significant technology trend. The course aims at familiarizing with the basic concepts of cloud computing and its applications.

Definition of Cloud Computing - Characteristics of Cloud Computing - Cloud Models - Service Models - Deployment Models - Cloud Services Examples - IaaS: Amazon EC2, Google Compute Engine, Azure VMs - PaaS: Google App Engine - SaaS: Salesforce - Cloud-based Services & Applications - Healthcare - Transportation Systems -  Manufacturing Industry – Government – Education - Mobile Communications

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

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 - Windows Azure SQL Database.

Application Services -  Content Delivery Services -  Analytics Services - Amazon Elastic MapReduce - Google MapReduce Service - Google BigQuery -  Deployment & Management Services - Amazon Elastic Beanstalk - Using and Managing Containers – Container Basics, Docker and Hub, Container for Science, Creating your own Container, Secure your VMs and Container.

Introduction - CSA Cloud Security Architecture – Authentication - Single Sign-on (SSO)- Authorization - Identity & Access Management - Data Security - Securing Data at Rest - Securing Data in Motion - Key Management - Auditing.

[2]. Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, CreateSpace Independent Publishing Platform, 2014.

[2]. Anthony TVelte, Toby JVelteand Robert Elsenpeter, Cloud Computing –A Practical Approach, Tata McGraw Hill Education Pvt Ltd, 2010.

[3]. Ian Foster and Dennis B. Gannon, “Cloud Computing for Science and Engineering”, MIT Press, 2017.

To provide deeper understanding into network security at the time of communication and security of data at premisses.  To insight in cyber law, its application, threats/vulnerabilities to networks and countermeasures. To gain knowledge about firewall, its working and Authentication mechanisms.

Network and Security Concepts: Information Assurance Fundamentals, Basic Cryptography, Symmetric Encryption, Public Key Encryption, The Domain Name System (DNS), Firewalls, Virtualization. Microsoft Windows Security Principles: Windows Tokens, Window Messaging, Windows Program Execution. Digital Signatures (DSS) – User Authentication: Kerberos Federated Identity Management - E-Mail Security - Pretty Good, Privacy - S/MIME – SSL - IP Security Overview

Concept of Cyber law and Cyber Space: Introduction - Meaning and Features of Cyber law - Significance and Advantages of Cyber Law - Meaning of Cyber Space - Inclusive of Cyber Space - Facilitating Functions of Cyber Space - Major Issues in Cyber Space. Need for an Indian Cyber law: Plans of National Information Technology Policy (NITP) - Need for Protection of data - Transactions in Security - Electronic Banking.

Mobile security, Cloud security, IoT security, Endpoint Detection and Response, Identity Management, Authentication, and Access Control, Firewalls, Data Security, Information Protection Processes and Procedures, Tools and Techniques for Detecting Cyber Incidents: Detect anomalies and events, Continuous monitoring, Detection Process. Cybersecurity Risk Management, Asset management, Governance, Risk assessment and management, Overview of NIST framework, Supply chain risk management, Manufacturing and Industrial Control systems security.

Self-Replicating Malicious Code; Evading Detection and Elevating Privileges: Obfuscation, Virtual Machine Obfuscation, Persistent Software Techniques, Rootkits, Spyware, Attacks against Privileged User Accounts and Escalation of Privileges, Token Kidnapping, Virtual Machine Detection, Stealing Information and Exploitation; Stealing Information and Exploitation: Form Grabbing, Man-in-the-Middle Attacks, DLL Injection, Browser Helper Objects.

Using Kali Linux: Linux command line, Data manipulation, Managing Networking, Netcat, Automating tasks, Programming: Bash Scripting, Information Gathering: Open source intelligence gathering, Port scanning, Finding vulnerabilities: Nessues, nMap, Web Application Scanning-Nikto, Capturing Traffic: Using Wireshark, DNS Cache poisoning, SSL attacks, Password Attacks: Password management, John the Ripper, Cracking Linux passwords, Web Application testing: SQL injection.

Introduction to tools:- 1. Wireshark ; 2. Kali Linux Tool; 3. Burp Suite; 4. Nmap (Network Mapper); 5. Metasploit; 6. John the Ripper.

2.James Graham, Richard Howard & Ryan Olson, “Cyber Security Essentials, CRC Press, Taylor and Francis Groups, 2011.

3.Penetration Testing: A Hands-On Introduction to Hacking Paperback, Georgia Weidman, 2014 edition.

4.Cybersecurity Risk Management: Mastering the Fundamentals Using the NIST Cybersecurity Framework, Cynthia Brumfield, 2022.

2.Jonathan Rosenoer, “Cyber law: The Law of Internet”, Springer Verlog, 1997 2. 

3.John W Ritting House, William M.Hancock, “Cyber Security Operations Handbook”, Read Elsevier 2008.

Software Project Management provides insight to the importance of careful project management. Topics are presented in the same order that they appear in the progression of actual projects 

Introduction to software project management and control whether software projects are different from other types of projects. Scope of project management - Management of project life cycle - Defining effective project objectives where there are multiple stakeholders - Software tools for project management - Project Planning: Creation of a project plan - step by step approach - The analysis of project characteristics in order to select the best general approach - Plan Execution - Scope Management - Use of Software (Microsoft Project) to Assist in Project Planning Activities.

Time Management, Project Network Diagram, Critical path Analysis, PERT, Use of Software (Microsoft Project) to Assist in Project Scheduling. Project Cost Management: Resource planning, Cost Estimation (Types, Expert Judgment, Estimation by Analogy, COCOMO).

Stages, Quality Planning, Quality Assurance, Quality Control, Quality Standards, Tools and Techniques for Quality Control.

Definition, Key to managing People, Organization Planning, Issues in Project Staff Acquisition and Team Development, Using Software to Assist in Human Resource Management, Communication Planning, Information Distribution, Performance Reporting.

Common Sources of Risk in IT projects, Risk Identification, Risk Quantification, Risk Response Development and Control. Project Procurement Management: Procurement Planning, Solicitation, Source Selection, Contract Administration

[2]. PankajJalote, “Software Project Management in Practice”, Pearson Education, 3rd Ed. ,

2010.

[3]. Kathy Schwalbe, “Information Technology Project Management”, THOMSON Course

Technology, International Student Edition, 2003.

[4]. Elaine Marmel, “Microsoft Office Project 2003 Bible”, Wiley Publishing Inc., 2003.

[2].     Robert T. Futrell, “Quality Software Project Management”, Pearson, 2010.

[3].     Bentley C. , “PRINCE2: A Practical Handbook”, NCC Blackwell, 2002.

[4].     Robert T. Futrell, “Quality Software Project Management”, Pearson, 2010.

[5].     S.A. Kelkar, “Software Project Management - A Concise Study”, PHI, Revised Edition, 2012.

 To provide a sound technical exposure to the concepts, principles, methods, and best practices in software architecture and software design.

Origin of an Architecture , Software Processes and Architectural Business Cycle, A good architecture, Software Architecture, What is & what it is not the software Architecture is, Other points of view, Architectural Pattern, Reference Models and Reference Architectures, The Importance of Software Architecture, Architectural structures & views, Case study in utilizing Architectural Structures.

Understanding the quality Attributes - Functionality and Architecture, Architecture and Quality Attributes - System Quality Attributes - Quality Attributes Scenarios in practice - Other System Quality Attributes - Business Qualities - Architecture Qualities - Achieving Qualities Introducing Tactics – Availability - Modifiability – Performance – Security – Testability – Usability - Relationships of Tactics to Architectural Patterns - Architectural Patterns and Style

Self Study: Achieving Qualities

Designing the Architecture: Architecture in the life cycle, Designing the Architecture, Forming the Team Structure, Creating the Skeletal System. Documenting Software Architectures, Uses of Architectural Documentation, Views, Choosing the relevant views, Documenting a view, Documentation across views.

ATAM (Architecture Tradeoff Analysis Method) :A comprehensive method for architecture evaluation, participants, outputs, phases of the ATAM, The Nightingale system - A case study in applying the ATAM.CBAM (Cost Benefit Analysis Method): A quantitative approach to architecture design decision making: Decision making context, basis for CBAM, Implementing CBAM.Architecture of ORACLE 12c, Java.

Reusing Architectural Assets: Overview – Successful working, Scope, Architectures and Difficulties in software product lines. Unix Architecture, Layered & State Diagram, Building systems from off-the-shelf components - Impact of components on Architecture, Architectural mismatch, Component-based design as search, ASEILM example.

Len Bass, Paul Clements, Rick Kazman, Software Architecture In Practice, Pearson Education Asia , 3rd Edition,2012.

[2]. Jeff Garland and Richard Anthony, “Large-Scale Software Architecture –  A  Practical Guide Using UML”, Wiley –Dreamtech India Pvt.,Ltd., 1st Edition,2002.

[3]. Pressman S Roger, “Software Engineering”, McGraw Hill International Editions, 7th Edition,2009.

[4]. Rumbaugh, James, “Object Oriented Modeling and design”, Pearson Education,  New  Delhi,2005.

To understand the need for Software Quality, Tools Used and Metrics involved.

To appreciate software testing principles and methods to detect in the ever changing software technological changes

Quality: popular view,professional view, software quality, total quality management, The defect prevention process, process maturity framework and quality standards (CMM , SPR Assessment, Malcolm Bridge, ISO9000).

Levels of measurement: some basic measures, reliability and validity , Software quality  metrics: Product Quality Metrics , in-process quality process , Example of Metrics Program – Motorola, HP.

Ishikawas’ seven basic tools: checklist, pareto diagram, histogram, runchart, scatter diagram control chart cause and effect diagram.Defect Removal Effectiveness: Literature review, a close look at DRE, defect removal effectiveness and quality planning.

Software Testing Principles, Economics of Testing Inspection and walkthrough, code inspection, an error checklist for Inspection, Walkthroughs, Desk Checking, Peer Rating Module Testing.

High Order Testing - Debugging by Brute Force, Induction, Deduction, Backtracking Extreme Programming basics, Extreme Testing, Extreme Testing Applied.

Object Oriented Analysis and Design Using UML course provides instruction and practical experience focusing on the effective use of object-oriented technologies and the judicious use of software modeling as applied to a software development process.

Complexity: The inherent complexity of software, The Structure of complex systems, Bringing order to chaos, on designing complex systems, Categories of analysis and Design methods. The Object Model: The evolution of object model, Elements of object model, applying the object model, Foundations of the object model.

Classes and Objects: The nature of an object - Relationship among objects, the nature of a class, Relationship among classes - The interplay of classes and objects, On building quality classes and objects, invoking a method - Classification: The importance of proper classification, Identifying classes and objects, Key abstraction and mechanisms, A problem of classification.

Notation: Basic BehaviouralModelling, Basic elements, class diagram, object, state Transition diagram, Interactions, Use Case Diagrams, Activity, module and process diagrams.

Principles, Micro and macro development process, Pragmatics- Management and planning, staffing, Release management, Reuse, Quality Assurance Metrics, Documentation, Tools, The benefits and risks and Object-oriented development.

Components, Deployment, Collaborations, Pattern and Frameworks, Component Diagram, Deployment Diagrams, Systems and Models

A domain based analysis and design using rational rose can be made

ESE: 40%

The objective of this course: is for students to learn how to design, prototype and evaluate user interfaces to effective browse and search systems by examining what research has uncovered, what developers have produced, and how people perform information tasks.

Human factors in user interface design, Theories, Principles, and Guidelines, Goals of Systems Engineering, Accommodation of Human Diversity, Goals for Our Profession, High Level Theories, Object-Action Interface model, Principle 1:Recognize the Diversity, Principle 2: Use the Eight Golden Rules of Interface Design, Principle 3: Prevent Errors, Guidelines for Data Display, Guidelines for Data Entry, Balance of automation and Human Control, Practitioner’s Summary, Researcher’s Agenda. 

Management Issues - Introduction, Organizational; Design to Support Usability, The three Pillars of Design, Development Methodologies, Ethnographic  Observation, Participatory Design, Scenario Development, Social Impact Statement for Early Design Review, Legal issues, Expert Reviews, Usability, testing and Laboratories, Surveys, Acceptance tests, Evaluation During Active Use, Controlled Psychologically Oriented Experiments, Practitioner’s Summary, Researcher’s agenda. 

Introduction, Specification Methods; Interface-Building Tools, Evaluation and critiquing Tools. Direct Manipulation and virtual Environments: Introduction, Examples of Direct manipulation systems, Explanations of Direct manipulation, Visual Thinking and Icons, Direct Manipulation Programming, Home Automation, Remote Direct manipulation, Virtual Environments Menus: Task-Related Organization, Item Presentation Sequence, Response Time and Display Rate.

Fasty Movement through Menus, Menu Layout, Form Filling, Dialog boxes, Command-Organization strategies, The Benefits of Structure, Naming and Abbreviations, Command Menus, Natural Language in Computing, Practitioners Summary, Researcher’s Agenda. 

Response Times, Styles and Manuals: Interaction Devices, Introduction, Keyboards and Function Keys, Pointing Devices, speech Recognition, Digitization, and Generation, Image and Video displays, Printers. Response Time and Display Rate: Theoretical; Foundations, Exceptions and attitudes, User Productivity, variability, Presentation Styles and Manuals: Introduction, Error messages, Nonanthopomorphic Design, Color of Manuals, Help: Reading From paper Versus from Displays, Preparation of Printed manuals,  Preparation of Online Facilities, Practitioner’s Summary, Researcher’s Agend.

Computer-Supported Cooperative work, Information’s search and www  Multiple-Windows Strategies: Introduction, Individual-Window Design, Multiple-window Design, Coordination by Tightly-Coupled Windows, Image Browsing and Tightly-Coupled Windows, Personal Role Management and Elastic Windows Computer-Supported Cooperative Work; Introduction, Goals of Cooperation, Asynchronous Interactions: Different Time, Different Place, Synchronous Distributed: Different Place, Same Time, Face to Face: Same Place, Same Time, Applying CSCW to Education.

Introduction, Database Query And Phrase Search in Textual Documents, Multimedia Document Searches, Information Visualization, Advanced Filtering. Hypermedia and the World wide Web: Introduction, Hypertext and Hypermedia, World Wide Web, Genres and Goals and Designers, Users and Their Tasks, Object Action Interface Model for Web Site Design, Practitioner’s summary, Researcher’s Agenda.

[2] Wilber O Galitz, An Introduction to GUI Design Principles and Techniques, John- Wiley &Sons, 2007.

[2] Alan Dix, Human-Computer Interaction, Pearson,2009.

ESE (Weightage): 40%

To familiarize the students with the concepts of computer graphics like line, circle drawing algorithms, transformations, clipping, projection, color models, curves. To make the students understand how to implement the computer graphics concepts using OpenGL.

Applications, Overview of Graphics Systems – Video display devices - Raster-scan systems - Graphics software - Introduction to OpenGL - Graphics Output Primitives Coordinate Reference Frames - Two-Dimensional frame in OpenGL - Point Functions - Line Functions - Line-Drawing Algorithms – DDA – Bresenhams - Curve Functions - Midpoint Circle Algorithm - and Display- window reshape function

Self-Learn: Area filling - Display lists - Basic colors - Attribute functions

Basic two-dimensional geometric transformations - Homogeneous Coordinates - Composite transformations - Geometric transformations in three-dimensional space – Translation - Rotation – scaling - composite three-dimensional transformations - OpenGL geometric transformation functions

Self-Learn: Reflection - shear

Light sources - Basic illumination models - transparent surfaces - OpenGL illumination functions

- Color Models - Standard primaries and chromaticity diagrams - RGB color model - HSV color model - OpenGL color functions

Self-Learn: Ray-tracing and Texture mapping

Two-dimensional viewing pipeline - clipping window - Normalization and viewport transformations - 2D viewing functions - Clipping Algorithms – Line clipping – Cohen- Sutherland and Liang-Barsky Line clipping - polygon clipping – Sutherland-Hodgman algorithm

- Three-dimensional viewing concepts – Projections - Three-dimensional viewing pipeline - Projection transformation - Parallel and Perspective projection matrices - 3D viewing functions

Self-Learn: Other clipping algorithms - Text clipping and Projection derivations

Spline representations - Cubic spline interpolation methods - Bezier curves and B-Spline curves- OpenGL approximation-Splinefunctions

[2]. Richard S Wright and Jr. Michael Sweet, “Open GL Super Bible:Comprehensive Tutorial and Reference”, 7nd Edition, Pearson Education, 2015.

This course provides an introduction to ANN with simple shallow neural network as a foundation for understanding the neuronal network concepts. As in detail it provides Convolution Neural Network algorithm, Recurrent Neural Network algorithm and Auto encoder algorithm along with various real time applications to explore as required by current industry standards.

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.

Connections-Learning Methods-Activation Functions-Importance Terminologies of ANN. Shallow neural networks- Difference between neural networks and deep neural networks – Applications of shallow neural network.

Introduction  - components  of  CNN  Architecture  -  Rectified  Linear  Unit  (ReLU)  Layer -Exponential  Linear  Unit  (ELU,  or SELU) - Unique Properties of CNN -Architectures of CNN -Applications of CNN.

Introduction- The Architecture of Recurrent Neural Network- The Challenges of Training Recurrent Networks- Echo-State Networks- Long Short-Term Memory (LSTM) - Applications of RNN.

Introduction - Features of Auto encoder Types of Autoencoder. Introduction to restricted Boltzmann machine.

[2] Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, September 2018.

[2] Francois Chollet, Deep Learning with Python, Manning Publications; 1st edition, 2017.

[3] John D. Kelleher, Deep Learning (MIT Press Essential Knowledge series), The MIT Press, 2019.

Web Resources:

[1] www.coursera.org

[2] http://neuralnetworksanddeeplearning.com

This course is to make students familiar with the concepts of the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning concepts.

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

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.

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.

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.

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

Language models for information retrieval, Language modeling versus other approaches in IR.

Graphical Models for Sequence Labelling in NLP.

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

[3] Introduction to Information Retrieval, Cambridge University Press. 2012.

[2] Steven Bird, Ewan Klein and Edward Loper Natural Language Processing with Python, O’Reilly Media; 1st edition, 2009.

 Web Resources:

[1] https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf

[2] https://nptel.ac.in/courses/106101007/

[3] NLTK – Natural Language Tool Kit-http://www.nltk.org

The goal of computer vision is to develop the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analyzed from a single image or a set of images. This course will cover the fundamentals of ComputerVision.

Digital image representation, Sampling and Quantization, Types of Images, Basic Relations between Pixels - Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations, Introduction to Digital Video, Sampled Video, Video Transmission.

Gray-Level Processing: Image Histogram, Linear and Non-linear point operations on Images, Arithmetic Operations between Images, Geometric Image Operations, Image Thresholding, Region labeling, Binary Image Morphology

Spatial domain-Linear and Non-linear Filtering, Introduction to Fourier Transform and the frequency Domain– Filtering in Frequency domain, Homomorphic Filtering, Brief introduction towards Wavelets, Wavelet based image denoising, A model of The Image Degradation / Restoration, Noise Models and basic methods for image restoration. Blotch detection and Removal.

Image Compression: Huffman Coding, Run length Coding, LZW Coding, Basics of Wavelets based image compression. Video Compression: Basic Concepts and Techniques of Video compression, MPEG-1 and MPEG-2 Video Standards.

Introduction to feature detectors, descriptors, matching and tracking, Basic edge detectors – canny, sobel, prewitt etc., Image Segmentation - Region Based Segmentation – Region Growing and Region Splitting and Merging, Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method.

Descriptors: Boundary descriptors - Fourier descriptors - Regional descriptors - Topological descriptors - moment invariants Object detection and recognition in image and video: Minimum distance classifier, K-NN classifier and Bayes, Applications in image and video analysis, object tracking in videos.

[2]. Alan Bovik, Handbook of Image and Video Processing, Second Edition, Academic Press, 2005.

[2] Richard Szeliski, Computer Vision–Algorithms and Applications, Springer,2011.

[3] Oge Marques, Practical Image and Video Processing Using MatLab, Wiley, 2011.

[4] John W. Woods, Multidimensional Signal, Image, Video Processing and Coding, Academic Press, 2006.

ESE: 40%

The objective of this course is to provide strong foundation of quantum computing concepts and their application to the field of applied machine learning.

An Overview of Quantum Machine Learning, States and Superposition, Density Matrix Representation and Mixed State, Composite Systems and Entanglement, Evolution.

Measurement, Uncertainty Relations, Tunneling, Adiabatic Theorem, No-Cloning Theorem, Qubits and the Bloch Sphere, Quantum Circuits, Adiabatic Quantum Computing, Quantum Parallelism, Grover's Algorithm, Complexity Classes, Quantum Information Theory.

Quantum Random Access Memory, Calculating Dot Products , Quantum Principal Component Analysis , Towards Quantum Manifold Embedding, Quantum K-Means . Quantum K-Medians, Quantum Hierarchical Clustering, Computational Complexity

Quantum Associative Memory The Quantum Perceptron, Quantum Neural Networks, Physical Realizations, Computational Complexity.

Nearest Neighbors, Support Vector Machines with Grover's Search, Support Vector Machines with Exponential Speedup, Computational Complexity.

Quantum Annealing, Quadratic Unconstrained Binary Optimization, Ising Model  QBoost, Nonconvexity Sparsity and Generalization Performance, Mapping to Hardware, Computational Complexity.

[2] Andreas Wichert, Principles of quantum artificial Intelligence, World Scientific, 2014.

[2] Robert S Sutor, Dancing With qbits, Packt Publishing 2019.

 Web Resources:

[1] https://qiskit.org/learn/

[2] https://pennylane.ai/qml/whatisqml.html

[3] https://www.tensorflow.org/quantum/concepts

On completion of the course students should have understood Software Agents and its Applications, Intelligent learning Methods.

Introduction to Software Agents: What is a software agent? - Why software agents? - Applications of Intelligent software agents-Practical design of intelligent agent systems.

Intelligent Agent Learning- Approaches to Knowledge base development-Disciple approach for building Intelligent agents- Knowledge representation-Generalization- Problem solving methods-Knowledge elicitation.

Rule learning problem- Rule learning method- Learned rule characterization. Rule refinement: Rule refinement problem- Rule refinement method- Rule experimentation and verification-Refined rule characterization-Agent interactions.

Disciple shell: Architecture of Disciple shell- Methodology for building Intelligent Agents- Expert-Agent interactions during knowledge elicitation process- Expert-Agent interactions during rule learning process- Expert-Agent interactions during rule refinement process.

Case studies in building intelligent agents: Intelligent Agents in portfolio management- Intelligent Agents in financial services- Statistical Analysis assessment and support agent- Design assistant for configuring computer systems.

Evolutionary Computing (EC) is a field of nature inspired computing that are mainly used for solving optimization problems. Major Evolutionary Algorithms (EAs) are Genetic Algorithms, Evolution Strategies, Genetic Programming, Differential Evolution, and Learning Classifier Systems. Applications of EA include evolution of engineering design, controls systems, automatic programming and many more. In this course, students will learn the basics of EA and understand the applications using online simulation tools. This course will give the concepts of evolutionary computing techniques and popular evolutionary algorithms that are used in solving optimization problems. Students will be able to implement custom solutions for real-time problems applicable with evolutionary computing.

Terminologies – Notations – Problems to be solved – Optimization – Modeling – Simulation – Search problems – Optimization constraints - Gentic Algorithms: History of genetics – Science of genetics – History of genetic algorithm – Simple binary genetic algorithm – continuous geneticalgorithm

Continuous evolutionary programming – Finite state machine optimization – Discrete evolutionary programming – The Prisoner’s dilemma - Evolution Strategy: One plus one evolution strategy – The 1/5 Rule – (μ+1) evolution strategy – Self adaptive evolution strategy

Fundamentals of genetic programming – Genetic programming for minimal time control - Evolutionary Algorithm Variation: Initialization – Convergence – Population diversity – Selection option – Recombination – Mutation

Pheromone models – Ant system – Continuous Optimization – Other Ant System - Particle Swarm Optimization: Velocity limiting – Inertia weighting – Global Velocity updates – Fully informed Particle Swarm

Pareto Optimality – Hyper volume – Relative coverage – Non-pareto based EAs – Pareto based EAs – Multi-objective Biogeography based optimization

[2]    D. Goldberg, Genetic algorithms in search, optimization, and machine learning. Boston: Addison-Wesley,2012.

[3]    K. Deb, Multi-objective optimization using evolutionary algorithms. Chichester: John Wiley & Sons, 2009.

[4]    R. Poli, W. Langdon, N. McPhee and J. Koza, A field guide to genetic programming. [S.l.]: Lulu Press,2008.

[5]    T. Bäck, Evolutionary algorithms in theory and practice. New York: Oxford Univ. Press, 1996.

Web Resources:

E. A.E and S. J.E, "Introduction to Evolutionary Computing | The on-line accompaniment to the book Introduction to Evolutionary Computing", Evolutionarycomputation.org, 2015. [Online]. Available: http://www.evolutionarycomputation.org/. [Accessed: 24- Jan-2020].

F. Lobo, "Evolutionary Computation 2018/2019", Fernandolobo.info, 2018. [Online]. Available: http://www.fernandolobo.info/ec1819/. [Accessed: 24- Jan-2020].

 "EC lab Tools", Cs.gmu.edu, 2008. [Online]. Available: https://cs.gmu.edu/~eclab/tools.html. [Accessed: 24- Jan- 2020].

 "Kanpur Genetic Algorithms Laboratory", Iitk.ac.in, 2008. [Online]. Available: https://www.iitk.ac.in/kangal/codes.shtml. [Accessed: 24- Jan-2020].

"Course webpage Evolutionary Algorithms", Liacs.leidenuniv.nl, 2017. [Online]. Available: http://liacs.leidenuniv.nl/~csnaco/EA/misc/ga_demo.htm. [Accessed: 24- Jan-2020].

ESE: 40%

It is a project to be taken up individually based on research or industrial problems

-

ESE 40%