Department of Computer Science and Engineering


Module 1 (10 (T) + 7(P) Hours)



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Module 1 (10 (T) + 7(P) Hours)


Programming Languages: Concepts and Constructs. Untyped Arithmetic Expressions – Introduction, Semantics, Evaluation.
Module 2 (10 (T) + 7(P) Hours)

Untyped Lambda Calculus – Basics, Semantics. Programming in Lambda Calculus.


Module 3 (10 (T) + 7(P) Hours)

Typed Arithmetic Expressions – Types and Typing relations, Type Safety.

Simply Typed Lambda Calculus – Function types, Typing relations, Properties of typing.
Module 4 (12 (T) + 7(P) Hours)

Extensions to Simply Typed Lambda Calculus – Unit type, Let bindings, Pairs, Records, Sums, Variants, References, Exceptions.


References:


  1. Benjamin C. Pierce, Types and Programming Languages, MIT Press, 2002

  2. David A. Schmidt, Programming Language Semantics. In Allen B. Tucker, Ed. Handbook of Computer Science and Engineering, CRC Press, 1996.

  3. Luca Cardelli, Type Systems. In Allen B. Tucker, Ed. Handbook of Computer Science and Engineering, CRC Press, 1996.

  4. Michael L. Scott, Programming Language Pragmatics, Elsevier, 2/e, 2004.



CS4023 COMPUTATIONAL INTELLIGENCE

Pre-requisite: Nil




L

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P

C

3

0

2

4

Total Hours: 70 Hrs  


Module 1 (10(T) + 7(P) Hours)


Artificial Intelligence: History and Applications, Production Systems, Structures and Strategies for state space search- Data driven and goal driven search, Depth First and Breadth First Search, DFS with Iterative Deepening, Heuristic Search- Best First Search, A* Algorithm, AO* Algorithm, Local Search Algorithms and Optimization Problems, Constraint satisfaction, Using heuristics in games- Minimax Search, Alpha Beta Procedure. Implementation of Search Algorithms in LISP

 

Module 2 (10(T) + 7(P) Hours)

Knowledge representation - Propositional calculus, Predicate Calculus, Forward and Backward chaining, Theorem proving by Resolution, Answer Extraction, AI Representational Schemes- Semantic Nets, Conceptual Dependency, Scripts, Frames, Introduction to Agent based problem solving. Implementation of Unification, Resolution and Answer Extraction using Resolution.
Module 3 (10(T) + 7(P) Hours)

 Machine Learning- Symbol based and Connectionist, Social and Emergent models of learning, Planning-Planning and acting in the real World, The Genetic Algorithm- Genetic Programming, Overview of Expert System Technology- Rule based Expert Systems, Introduction to Natural Language Processing. Implementation of Machine Learning algorithms.


Module 4 (12(T) + 7(P) Hours)

 Languages and Programming Techniques for AI- Introduction to PROLOG and LISP, Search strategies and Logic Programming in LISP, Production System examples in PROLOG.


References:

1. George F Luger, Artificial Intelligence- Structures and Strategies for Complex Problem Solving, 4/e, Pearson Education, 2002.

2. E. Rich and K.Knight, Artificial Intelligence, 2/e, Tata McGraw Hill, 1996.

3. S Russel and P Norvig, Artificial Intelligence- A Modern Approach, 2/e, Pearson Education, 2002

4. Nils J Nilsson, Artificial Intelligence a new Synthesis, Elsevier, 1998.

5. Winston. P. H, LISP, Addison Wesley, 1982.

6. Ivan Bratko, Prolog Programming for Artificial Intelligence, 3/e, Addison Wesley, 2000.

7. Dr.Russell Eberhart and Dr.Yuhui shi, Computational Intelligence - Concepts to Implementation, Elsevier, 2007.

8. Fakhreddine O Karray, Clarence De Silva, Soft Computing and Intelligent Systems Design- Theory tools and Applications, Pearson Education, 2009.

CS4024 INFORMATION THEORY

Pre-requisite: Nil




L

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4

0

0

4

Total Hours: 56 Hrs  


Module 1 (14 Hours)


Foundations: Review of probability theory, entropy and information, random sources, i.i.d and Markov sources, discrete finite state stationary Markov sources, Entropy rate of stationary sources, Computation of stationary distributions.
Module 2 (14 Hours)

Source Coding: Prefix and uniquely decodable codes - Kraft's and Macmillan's inequalities - Shannon's source coding theorem - Shannon Fano code, Huffman code - optimality - Lempel Ziv code - optimality for stationary ergodic sources.


Module 3 (14 Hours)

Channel Coding: BSC and BEC channel models - Channel capacity - Shannon's channel coding theorem - existence of capacity achieving codes for BEC, Fano-Elias Inequality.


Module 4 (14 Hours)

Cryptography: Information theoretic security - Perfect secrecy - Shannon's theorem - perfectly secret codes - Introduction to computational security and pseudo random sources.


References:

  1. T. M. Cover and J. A. Thomas, Elements of Information Theory, Addison Wesley, 1999.

  2. D. J. Mackay, Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2002.

  3. H. Delfs and H. Knebl, Introduction to Cryptography, 2/e, Springer, 2010.



CS4025 GRAPH THEORY AND COMBINATORICS

Pre-requisite: Nil




L

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4

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4

Total Hours: 56 Hrs  

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