Content

Hrs

00

Artificial Intelligence :

Course Content                                

01

Introduction to AI :

Definitions, Goals of AI, AI Approaches, AI Techniques, Branches of AI, Applications of AI.          

1-6

02

Problem Solving, Search and Control Strategies :

General problem solving , Search and control strategies , Exhaustive searches ,  Heuristic search techniques, Constraint satisfaction problems (CSPs), models .

7-14

03

Knowledge Representation Issues,  Predicate Logic, Rules :  

Knowledge representation, KR using predicate logic, KR using rules .

15-22

04

Reasoning  System  - Symbolic, Statistical :

Reasoning ,  Symbolic reasoning, Statistical reasoning .

23-28

05

Game Playing :

Overview, Mini-Max search procedure, Game playing with Mini-Max, Alpha-Beta  pruning .

29-30

06                  

Learning :

What is learning, Rote learning, Learning from example : Induction, Explanation Based Learning (EBL), Discovery, Clustering , Analogy, Neural net and genetic learning, Reinforcement learning .

31-34

07

Expert System :

Introduction, Knowledge acquisition, Knowledge base, Working memory, Inference engine, Expert system shells, Explanation, Application of expert systems .

35-36

08

Fundamentals of Neural Networks :

Introduction and research history, Model of artificial neuron, neural network Characteristics, Learning methods, Single-layer network system,  Applications.

37-38

09

Fundamentals of  Genetic  Algorithms :

Introduction,  Encoding, Operators of genetic algorithm,  Basic genetic algorithm .

39-40

10

Natural Language Processing :

Introduction,  Syntactic processing, Semantic and pragmatic analysis .

41

11          

Common Sense :

Introduction,  Physical world, Common sense ontologies, Memory organization .

42