6.867 Machine Learning (Fall 2004)






Problem sets




Fall 2003
Fall 2002
Fall 2001


This introductory course on machine learning will give an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as 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 2:30-4pm, Stata Center 32-141

Instructor: Professor Tommi Jaakkola
tommi@csail.mit.edu, Stata Center 32-G498, tel x3-0440

Teaching assistants:

Biswajit (Biz) Bose
cielbleu@mit.edu, Stata Center 32-D528, tel 3-6095, office hours: Thursdays 4-5pm and Fridays 4-5pm in (near) 32-D528

Adrian Corduneanu
adrianc@mit.edu, Stata Center 32-G472, office hours: Tuesdays 3-5pm in 24-316


Lecture slides and supplementary notes will be made available electronically.

There isn't a single textbook that covers all the material in the course but we recommend the following books:

  • M. Jordan. Introduction to Probabilistic Graphical Models (this draft version is available only within MIT, do not distribute)
  • C. Bishop. Neural Networks for Pattern Recognition
  • Duda, Hart, Stork. Pattern Classification (2rd edition).
  • Other reading material such as papers will be made available electronically.