MY TEACHING

Introduction: Definition and Importance of AI, Foundations, Overview and Historical Background, Current AI Systems.

​

Intelligent Agents: Agents and environment, Rationality, Nature of Environment, Different types of agents, Structure of Agents.

​

Searching: Toy Problems, Searching, Tree Search and Graph Search, Uninformed Search, Breadth First Search, Depth First Search, Depth-Limited Search, Iterative Deepening Depth First Search, Bidirectional Search.

​

Informed Search: Informed/Heuristic Search, A* Search, Heuristic Functions, Best first Search, Hill-Climbing Search, Simulated Annealing, Local Beam Search, Tabu Search, Genetic Algorithm.

​

Problem Decomposition: Goal Tree, AO* Search.

​

Constraint Satisfaction Problems: Constraint Satisfaction Problems, Backtracking Search, Minimum Remaining Values Heuristic, Most Constraint Variable Heuristic, Least Constraining Value Heuristic, Forward Checking, Constraint Propagation.

​

Planning: Planning problem, Goal Stack Planning, Plan Space Planning, Graph Plan Algorithm.

​

Learning: Forms of learning (an overview), Supervised & Unsupervised Learning, Decision Trees, Reinforcement Learning, Passive Reinforcement Learning, Active Reinforcement Learning.

​

Adversarial Search: Games, Optimal decisions in games, Minimax Algorithm, Alpha-Beta Algorithm, Cut-off Search.

​

Uncertainty: Uncertainty, Probability Basics, Axioms of Probability, Inference using Full Jointb Distributions, Independence, Baye's rule and its use.

​

Logic and Inferences: Propositional Logic, First Orderv Logic, Soundness and Completeness, Introduction to Prolog: Basic Constructs, Controlling Backtracking, Answer Extraction.

​

Expert Systems: Introduction to knowledge based expert systems, Knowledge representation (using rules, frames, semantics) and reasoning, Rule based expert systems, Conflict resolution, Frame based Expert Systems, Neural Expert Network Systems (covering basics of ANN).