Data communication Components (8 Hours)

Representation of data and its flow Networks , Various Connection Topology, Protocols and Standards, OSI model, Transmission Media, LAN: Wired LAN, Wireless LANs, Connecting LAN and Virtual LAN, Techniques for Bandwidth utilization: Multiplexing - Frequency division, Time division and Wave division, Concepts on spread spectrum.

Data Link Layer and Medium Access Sub Layer (8 Hours)

Error Detection and Error Correction - Fundamentals, Block coding, Hamming Distance, CRC; Flow Control and Error control protocols - Stop and Wait, Go back – N ARQ, Selective Repeat ARQ, Sliding Window, Piggybacking, Random Access, Multiple access protocols - Pure ALOHA, Slotted ALOHA, CSMA/CD,CDMA/CA.

Network Layer (8 Hours)

Switching, Logical addressing – IPV4, IPV6; Address mapping - ARP, RARP, BOOTP and DHCP–Delivery, Forwarding and Unicast Routing protocols.

Transport Layer (8 Hours)

Process to Process Communication, User Datagram Protocol (UDP), Transmission Control Protocol (TCP), SCTP Congestion Control; Quality of Service, QoS improving techniques: Leaky Bucket and Token Bucket algorithm.

Application Layer (8 Hours)

Domain Name Space (DNS), DDNS, TELNET, EMAIL, File Transfer Protocol (FTP), WWW, HTTP, SNMP, Bluetooth, Firewalls, Basic concepts of Cryptography.

Suggested Books

1. Data Communication and Networking, 4 th Edition, Behrouz A. Forouzan, Mc GrawHill.
2. Data and Computer Communication, 8 th Edition, William Stallings, Pearson Prentice Hall India.
1. Computer Networks, 8th Edition, Andrew S. Tanenbaum, Pearson New International Edition.
2. Internetworking with TCP/IP, Volume 1, 6 th Edition Douglas Comer, Prentice Hall of India.

Introduction ( 6 Lectures )

Lexical Analysis (scanner): Regular languages, finite automata, regular expressions, from regular expressions to finite automata, scanner generator (lex, flex).
Phase of compilation and overview.

Syntax Analysis (Parser) ( 9 Lectures )

Context-free languages and grammars, push-down automata, LL(1) gram-mars and top-down parsing, operator grammars, LR(O), SLR(1), LR(1), LALR(1) grammars and bottom-up parsing, ambiguity and LR parsing, LALR(1) parser generator (yacc, bison)

Symantic Analysis and Symbol Table ( 10 Lectures )

Attribute grammars, syntax directed definition, evaluation and flow of attribute in a syntax tree.
Symbol Table: Its structure, symbol attributes and management. Run-time environment: Procedure activation, parameter passing, value return, memory allocation, and scope.

Code Generation and Architecture Improvement ( 10 Lectures )

Intermediate Code Generation: Translation of different language features, different types of intermediate forms.
Code Improvement (optimization) Analysis: control-flow, data-flow dependence etc.; Code improvement local optimization, global optimization, loop optimization, peep-hole optimization etc.
Architecture dependent code improvement: instruction scheduling (for pipeline), loop optimization (for cache memory) etc. Register allocation and target code generation.

Advanced Topics ( 5 Lectures )

Type systems, data abstraction, compilation of Object Oriented features and non-imperative programming languages.

Suggested Books

CompilersPrinciples.Techniques. AndToolsbyAlfredV.Aho. RaviSethiJefferyD.Ullman. PearsonEducation.
2. Compiler Design by Santanu Chattopadhyay. PHI
3. Modern Compiler Design by Dick Grune . E. Bal. Ceriel J. H. Jacobs. And Koen G. Langendoen Viley Dreamtech.

Introduction ( 8 Hrs )

Basic definitions, Linear Algebra, Statistical learning theory, types of learning, hypothesis space and Inductive bias, evaluation and cross validation, Optimization.

Statistical Decision Theory, Bayesian Learning (ML, MAP, Bayes estimates, Conjugate priors), Linear Regression, Ridge Regression, Lasso, Principal Component Analysis, Partial Least Squares.

Linear Classification, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Perceptron, Support Vector Machines + Kernels, Artificial Neural Networks + Back Propagation, Decision Trees, Bayes Optimal Classifier, Naive Bayes.

Hypothesis testing, Ensemble Methods, Bagging Adaboost Gradient Boosting, Clustering, K- means, K-medoids, Density-based Hierarchical, Spectral .

Expectation Maximization, GMMs, Learning theory Intro to Reinforcement Learning , Bayesian Networks.

Signal and System

Graph Theory

Computer Graphics

Introduction To Java Programming Language

Probability and Statistical Inference

Numerical Methods

Information Theory and Coding

Soft Computing

Distributed Database

Advanced Data Structures and Algorithms

Advance Java Programming Language

Web and Internet Technology

Multimedia Technology and its Applications

Cryptography and Network Security

Mobile and Wireless Computing

Make project your own

Website development using PHP ( HTML, XHTML, XML, JavaScript, CSS[Bootstrap]) laboratory

Python Programming.

Working on MATLAB

Working on Android Studio

Working with UNIX/ LINUX