Artificial Intelligence

Overview

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: an overview of different forms of learning, learning decision trees, neural networks; Introduction to natural language processing.

Syllabus

Lecture - 1 Introduction to Artificial Intelligence.
Lecture - 2 Intelligent Agents.
Lecture - 3 State Space Search.
Lecture - 4 Uninformed Search.
Lecture - 5 Informed Search.
Lecture - 6 Informed Search - 2.
Lecture - 7 Two Players Games - I.
Lecture - 8 Two Players Games - II.
Lecture - 9 Constraint Satisfaction Problems - 1.
Lecture - 10 Constraint Satisfaction Problems 2.
Lecture - 11 Knowledge Representation and Logic.
Lecture - 12 Interface in Propositional Logic.
Lecture - 13 First Order Logic.
Lecture - 14 Reasoning Using First Order Logic.
Lecture - 15 Resolution in FOPL.
Lecture - 16 Rule Based System.
Lecture - 17 Rule Based Systems II.
Lecture - 18 Semantic Net.
Lecture - 19 Reasoning in Semantic Net.
Lecture - 20 Frames.
Lecture - 21 Planning - 1.
Lecture - 22 Planning - 2.
Lecture - 23 Planning - 3.
Lecture - 24 Planning - 4.
Lecture - 25 Rule Based Expart System.
Lecture - 26 Reasoning with Uncertainty - I.
Lecture - 27 Reasoning with Uncertainty - II.
Lecture - 28 Reasoning with Uncertainty III.
Lecture - 29 Reasoning with Uncertainty - IV.
Lecture - 30 Fuzzy Reasoning - I.
Lecture - 31 Fuzzy Reasoning - II.
Lecture - 32 Introduction to Learning - I.
Lecture - 33 Introduction to Learning - II.
Lecture - 34 Rule Induction and Decision Trees - I.
Lecture - 35 Rule Induction and Decision Trees - II.
Lecture - 36 Leavning 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.