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• Solutions to the final exam.

This introductory course on machine learning will give an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Lectures: Mon and Wed 1-2.30pm in 54-100
Recitation: Friday 10am (26-210), 11am (26-210), 2pm (26-328), 3pm (26-328)

Instructor: Professor Tommi Jaakkola (tommi at csail dot mit dot edu)
Stata Center 32-G498, tel x3-0440
Office hours: Tuesdays, 11am-noon, 32-G498 (until Dec 11)

Teaching assistants (6867-tas@lists.csail.mit.edu):

Daniel Roy (droy at mit dot edu)
Stata Center 32-G496
Office Hours: Thursday, 11-12:30pm or by appointment

John Barnett (barnett at mit dot edu)
Stata Center 32-G496
Office hours: Wednesdays 4-5pm in 32-G4, or by appointment

Text/material:

Lecture notes (a few pages per lecture) and supplementary notes will be made available electronically.

There isn't a single textbook that covers most of the material in the course but there are a number of books with some overlap.

• Bishop, "Neural Networks for Pattern Recognition", 1995
• Duda, Hart, Stork, "Pattern Classification", 2000
• Hastie, Tibshirani and Friedman, "Elements of Statistical Learning: Data Mining, Inference and Prediction", 2001
• MacKay, "Information Theory, Inference, and Learning Algorithms", 2003.
  Available on-line here
• Mitchell, "Machine Learning", 1997.