Fundamentals of Artificial Intelligence

What does automatic scheduling or autonomous driving have in common with web search, speech recognition, and machine translation? These are complex real-world problems that span across various practices of engineering! The aim of artificial intelligence (AI) is to tackle these problems with rigorous mathematical tools. The objective of this course is to present an overview of the principles and practices of AI to address such complex real-world problems. The course is designed to develop a basic understanding of problem solving, knowledge representation, reasoning and learning methods of AI.

INTENDED AUDIENCE: Final Year B.Tech; M.Tech and PhD
PREREQUISITES: Basic Course in Probability and Linear Algebra

Fundamentals of Artificial Intelligence [Introduction].
Lec 01: Introduction to AI.
Lec 02: Problem Solving as State Space Search.
Lec 03: Uniformed Search.
Lec 04: Heuristic Search.
Lec 05: Informed Search.
Lec 06: Constraint Satisfaction Problems.
Lec 07: Searching AND/OR Graphs.
Lec 08: Game Playing.
Lec 09: Minimax + Alpha-Beta.
Lec 10: Introduction to Knowledge Representation.
Lec 11: Propositional Logic.
Lec 12: First Order Logic -I.
Lec 13: First Order Logic -II.
Lec 14: Inference in First Order Logic - I.
Lec 15: Inference in FOL - II.
Lec 16: Answer Extraction.
Lec 17: Procedural Control of Reasoning.
Lec 18: Reasoning under Uncertainty.
Lec 19: Bayesian Network.
Lec 20: Decision Network.
Lec 21: Introduction to Planning.
Lec 22: Plan Space Planning.
Lec 23: Planning Graph and GraphPlan.
Lec 24: Practical Planning and Acting.
Lec 25: Sequential Decision Problems.
Lec 26: Making Complex Decisions.
Lec 27: Introduction to Machine Learning.
Lec 28: Learning Decision Trees.
Lec 29: Linear Regression.
Lec 30: Support Vector Machines.
Lec 31: Unsupervised Learning.
Lec 32: Reinforcement Learning.
Lec 33: Learning in Neural Networks.
Lec 34: Deep Learning: A Brief Overview.