CMS-A-DSE-B--3-TH:Introduction to Computational Intelligence DSE-B: Choice-3, Theory, Credit:04, Contact hours: 60.

 Introduction

Introduction to Artificial Intelligence, Brief History and Application, Structures and

Strategies for state space search- Data driven and goal driven search, Heuristic search,

Depth First and Breadth First search, Iterative deepening, A* algorithm, Game playing

(Minimax), Rule-based system, Semantic Nets, Frames, Scripts, Conceptual

Dependency, Introduction to PROLOG.

20 hours

Neural Network

Basics of Artificial Neural Network, Characteristics and Comparison with biological

neural network, Basic model of Artificial Neural Network: Single layer Perceptron

model, Learning, Feed Forward Neural Network, Error, Back Propagation and weight

updation, Perceptron, Bayesian Networks, Neural computational model- Hopfield Nets.

.

20 hours

Rough sets

Basic difference between Rough sets and Fuzzy sets

02 hours

Fuzzy Logic and Application

Fuzzy sets, application – basic operations, Properties, Fuzzy Relations, Fuzzy

inference, Notion of Fuzziness, Operations on Fuzzy sets, Fuzzy Numbers, Brief

overview of crisp sets, Crisp relations, Fuzzy relations, Max*-composition of fuzzy

relation, Max*-transitive closure, Probability measures of fuzzy events, Fuzzy expected

value, Approximate reasoning, Different methods of role aggregation and

defuzzification.