Course Outline

The Artificial Intelligence course will cover the following:

1. Introduction to AI
2. Definitions, Goals of AI, AI Approaches, AI Techniques, Branches of AI,Applications of AI.
3. Problem Solving, Search and Control Strategies:
4. General problem solving, Search and control strategies, Exhaustive searches, Heuristic search techniques, Constraint satisfaction problems (CSPs) and models.
5. Knowledge Representations Issues, Predicate Logic, Rules:
6. Knowledge representation, KR using predicate logic, KR using rules.
7. Learning Systems:
8. Rote learning, Learning from example: Induction, Explanation Based Learning (EBL), Discovery, Clustering, Analogy, Neural net and genetic learning, Reinforcement learning.
9. Expert Systems:
10. Knowledge acquisition, Knowledge base, Working memory, Inference engine, Expert system shells, Explanation, Application of expert systems.
11. Fundamentals of Neural Networks:
12. Research history, Model of artificial neuron, Neural networks architectures, Learning methods in neural networks, Single-layer neural network system, Applications of neural networks.
13. Fundamentals of Genetic Algorithms:
14. Search optimization algorithm, Evolutionary algorithm, Encoding, Operators of genetic algorithm, Basic genetic algorithm.
15. Natural Language Processing:
16. Introduction, Syntactic processing, Semantic and Pragmatic analysis.