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:
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Benjamin C. Pierce, Types and Programming Languages, MIT Press, 2002
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David A. Schmidt, Programming Language Semantics. In Allen B. Tucker, Ed. Handbook of Computer Science and Engineering, CRC Press, 1996.
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Luca Cardelli, Type Systems. In Allen B. Tucker, Ed. Handbook of Computer Science and Engineering, CRC Press, 1996.
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Michael L. Scott, Programming Language Pragmatics, Elsevier, 2/e, 2004.
CS4023 COMPUTATIONAL INTELLIGENCE
Pre-requisite: Nil
-
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
-
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:
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T. M. Cover and J. A. Thomas, Elements of Information Theory, Addison Wesley, 1999.
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D. J. Mackay, Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2002.
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H. Delfs and H. Knebl, Introduction to Cryptography, 2/e, Springer, 2010.
CS4025 GRAPH THEORY AND COMBINATORICS
Pre-requisite: Nil
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Total Hours: 56 Hrs
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