Unit 1 Introduction

• What is artificial Intelligence? Major areas of Artificial Intelligence
• Introduction to AI Problems and applications
• Defining problems as a state space search
• Production systems. 3 7.5

Unit 2 Search techniques

6 lecture hours

• Breadth first search
• Depth first search
• Hill climbing
• Best first search
• AO* Algorithm
• Iterative Deepening Search
• Recursive Best First Search
• Constraint Satisfaction and Heuristic Repair

Unit 3 Game Playing

4 lecture hours

• Introduction to Game playing
• The Minimax Search Procedure
• Alpha- Beta Procedure
• The Search Efficiency of Alpha-Beta Procedure
• Recent applications

Unit 4 Knowledge Representation

4 lecture hours

• Production rules
• Predicate Calculus- Rules of Inference
• Semantics and Deduction
• Soundness and completeness of rules
• Resolution refutation
• Semantic Nets
• symbolic reasoning
• statistical reasoning

Unit 5 Learning

• Rote learning
• learning by taking advice
• learning in problem solving
• learning from examples

Unit 6 Uncertain Reasoning

15 lecture hours

• 6 Joint probability
• Marginal probability
• Probabilistic reasoning and Bayes Nets
• forward reasoning versus backward reasoning
• Certainty Factors
• Fuzzy set theory
• Fuzzy relation
• Fuzzy value assignment methods
• Inference and Composition methods- Min-Max composition
• max product composition
• Defuzzification methods
• Applications and recent developments

Unit 7 Statistical Methods

• Frequency distributions
• Data analysis
• Expectations and moments
• Co- relation and regression
• Trend analysis
• Seasonal effects
• Cyclical fluctuation
• Moving average, MSE, Predictions 3 7.5

Unit 8 Expert Systems (ES)

4 lecture hours

• Advantages and characteristics of Expert System
• Knowledge engineering
• Steps in Developing an Expert System
• ES Applications and recent developments

Unit 9 Connectionist Models

6 lecture hours

• Introduction to Neural Network
• Activation functions
• Supervised and Unsupervised Learning
• Neuro Processing and Neural Network Learning
• Learning rules
• Single layer Perceptrons and Classification
• Introduction to Multilayer Neural Networks
• Neural Network Applications and recent developments