(This course was renumbered as of Fall 2007. Previous versions of this course were numbered 15-681.) Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to spot high-risk medical patients, recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Data Mining, Bayesian networks, decision tree learning, neural network learning, statistical learning methods, and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor. Short programming assignments include hands-on experiments with various learning algorithms. Typical assignments include learning to automatically classify email by topic, and learning to automatically classify the mental state of a person from brain image data. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. This class is intended for Masters students and advanced undergraduates.