6.867 Machine Learning (Fall 2003)





Problem sets




Fall 2002
Fall 2001

THIS IS FALL 2003 SITE .. please use the current site instead


  • Final exam: in class, Wed 12/10

  • 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/Wed 2:30-4pm in 35-225

    Instructor: Professor Tommi Jaakkola
    tommi@ai.mit.edu, room NE43-735, tel x3-0440

    Teaching assistant: Jason Johnson
    jasonj@mit.edu, room 35-439, tel x3-6172, office hours Mon/Thu 10-12

    Teaching assistant: Nathan Srebro
    nati@mit.edu, room NE43-741, office hours Thu 2pm-4pm

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

  • M. Jordan and C. Bishop Introduction to 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.