Course Contents
Module 1: Introduction to AI (4 hours)
- Definition and goals of AI
- History and evolution
- Types of AI: Narrow, General, Super
- Applications in various domains
- Ethical considerations in AI
Module 2: Intelligent Agents (4 hours)
- Agents and environments
- PEAS (Performance, Environment, Actuators, Sensors)
- Agent types: simple reflex, model-based
- Structure of intelligent agents
- Rationality and autonomy
Module 3: Uninformed Search Techniques (8 hours)
- Problem formulation
- Breadth-first search (BFS)
- Depth-first search (DFS)
- Uniform cost search
- Depth-limited and iterative deepening search
Module 4: Informed Search and Heuristics (8 hours)
- Best-first search
- A* algorithm
- Heuristic functions and their properties
- Hill climbing
- Simulated annealing and local search
Module 5: Game Playing (6 hours)
- Adversarial search concepts
- Minimax algorithm
- Alpha-beta pruning
- Evaluation functions
- Game trees and cutoffs
Module 6: Knowledge Representation (8 hours)
- Propositional logic
- First-order predicate logic
- Unification and resolution
- Inference mechanisms
- Forward and backward chaining
Module 7: Expert Systems and Applications (6 hours)
- Rule-based systems
- Architecture of expert systems
- Case studies: MYCIN, DENDRAL
- Agent architectures
- Chatbots and AI applications