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

Lecture 19 - Reasoning in Semantic Net

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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