Course Material

Required Text:

• Machine Learning by Tom Mitchell, McGraw Hill, 1997
• Introduction to Machine Learning by Ethem Alpaydin, MIT Press, Second Edition, 2009

Optional Text:

• All of Statistics by Larry Wasserman, Springer, 2010
• Reinforcement Learning: An Introduction by Richard Sutton and Andrew Barto, MIT Press, 1998
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, 2009

Supplemental Material

• Udacity's Online Machine Learning Course by Prof. Charles Isbell and Prof. Michael Littman
• Andrew Moore's Tutorial Slides on Machine Learning
• Machine Learning Algorithm Cheat Sheet
• Neural Networks
• Neural Network Design Guidelines
• Support Vector Machines
• SVM Tutorial
• Information Theory
• Charles Isbell's Notes on Information Theory
• Dimensionality Reduction
• Independent Component Analysis
• Clustering
• Expectation Maximization Algorithm
• Impossibility Theorem for Clustering
• Reinforcement Learning
• Reinforcement Learning: A Survey
• Q-Learning