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Artificial Intelligence

Artificial Intelligence. Instructors: Prof. Anupam Basu and Prof. Sudeshna Sarkar, Department of Computer Science and Engineering, IIT Kharagpur. The course will cover basic ideas and techniques underlying the design of intelligent computer systems. Topics include: Introduction to AI and intelligent agents; Solving problems by searching, heuristic search techniques, constraint satisfaction problems, stochastic search methods; Knowledge and reasoning: propositional logic, first order logic, situation calculus; Theorem proving in first order logic; Planning, partial order planning; Uncertain knowledge and reasoning; Learning: overview of different forms of learning, learning decision trees, neural networks; Introduction to natural language processing. (from nptel.ac.in)

Lecture 08 - Two Player Games: Game Tree Search (cont.)

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Lecture 01 - Introduction to Artificial Intelligence

Lecture 02 - Intelligent Agents

Lecture 03 - State Space Search

Lecture 04 - Uninformed Search: Depth First Search, Breadth First Search, Iterative Deepening Search

Lecture 05 - Informed Search: Heuristics - A*, Greedy Search, Uniform-Cost Search

Lecture 06 - Informed Search: A* Search, Iterative-Deepening A*

Lecture 07 - Two Player Games: Game Tree Search

Lecture 08 - Two Player Games: Game Tree Search (cont.)

Lecture 09 - Constraint Satisfaction Problems

Lecture 10 - Constraint Satisfaction Problems (cont.)

Lecture 11 - Knowledge Representation and Logic: Propositional Logic

Lecture 12 - Interface in Propositional Logic

Lecture 13 - First Order Logic

Lecture 14 - Reasoning using First Order Logic

Lecture 15 - Resolution in First Order Predicate Logic

Lecture 16 - Rule Based Systems

Lecture 17 - Rule Based Systems (cont.)

Lecture 18 - Semantic Net

Lecture 19 - Reasoning in Semantic Net

Lecture 20 - Frames

Lecture 21 - Introduction to Planning Problems

Lecture 22 - Planning: Forward Search, Backward Search, Strips Planning, Partial Order Planning

Lecture 23 - Planning: Partial Order Planning (cont.)

Lecture 24 - Planning Graph and Graphplan Algorithm

Lecture 25 - Rule Based Expert System

Lecture 26 - Reasoning with Uncertainty: Certainty Factors

Lecture 27 - Reasoning with Uncertainty: Certainty Factors (cont.)

Lecture 28 - Reasoning with Uncertainty: Basics of Probability Theory

Lecture 29 - Reasoning with Uncertainty: Bayes' Rule, Belief Network, Conditional Independence

Lecture 30 - Fuzzy Reasoning: Fuzzy Logic, Fuzzy Sets

Lecture 31 - Fuzzy Reasoning: Fuzzy Sets (cont.)

Lecture 32 - Introduction to Learning - Definition of Machine Learning, Concept Learning

Lecture 33 - Introduction to Learning - The Problem of Concept Learning

Lecture 34 - Rule Induction and Decision Trees I

Lecture 35 - Rule Induction and Decision Trees II

Lecture 36 - Learning using Neural Networks I

Lecture 37 - Learning using Neural Networks II

Lecture 38 - Probabilistic Learning

Lecture 39 - Natural Language Processing I

Lecture 40 - Natural Language Processing II