15-381 Artificial Intelligence; Lectures

Spring 2008, Carnegie Mellon Qatar

Introduction

This page lists all of the lectures and readings associated with the course. These lecture notes were created by the authors. They make significant use of the prior course material for the 15-381 class at Carnegie Mellon, the recommended AI textbook by Russell & Norvig, as well as the online resources of Andrew Moore, and the associated repository for the textbook.

Lectures

Lecture	Slides	Extras
Week 1a	Introduction to AI and Search
Week 1b	Uninformed Search
Week 2a	Informed Search
Week 2b	Local Search
Week 3a	Probability Theory
Week 3b	Probability Theory 2	Joint vs Naive Density Estimation Example: Octave Normalizing a Gaussian pdf tutorial, we also have a Local copy
Week 4a	Bayes Nets 1
Week 4b	Bayes Nets 2
Week 4a	Decision Trees 1
Week 4b	Decision Trees 2
Week 5a	Probabilistic Classification
Week 5b	Clustering
Week 6a	Gaussian Mixture Models, PCA	EM Tutorial by Frank Dellaert Andrew Moore's MLE tutorial Andrew Moore's GMM tutorial
Week 6b	Break!
Week 7a	Markov Decision Processes	Introduction to Reinforcement Learning online version of Sutton & Barto's book. AI Book, python code repository (has MDP example)
Week 7b	Reinforcement Learning	Introduction to Reinforcement Learning online version of Sutton & Barto's book. Andrew Moore/Auton Lab demo software for MDP solving/Reinforcement learning Reinforcement learning review Journal paper.
Week 8a	Motion Planning
Week 8b	Neural Networks
Week 9a	Constraint Satisfaction Problems (CSP)
Week 9b	Cancelled due to programming competition.
Week 10a	CSPs and Classification2
Week 10b	Zero-sum Games
Week 11	Non-zero sum Games

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