6.867 Machine Learning

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Fall 2002

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General info


Class project

Problem sets



Last year's course

This introductory course on machine learning will give an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons 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 and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.

There is a number of texts that we recommend for the course.
  • M. Jordan and C. Bishop Introduction to Graphical Models (draft version for MIT only access, do not distribute).
  • C. Bishop. Neural Networks for Pattern Recognition
  • Duda, Hart, Stork. Pattern Classification (3rd edition).
  • Other reading material such as papers will be made available electronically.

    Lectures are Tue/Thu 2:30-4pm in 37-212.

    There are two (identical) weekly tutorial sections:
    Wed11am-12pm 38-166

    Contact Information

    Instructor Professor Tommi Jaakkola
    Office hours: Thursday 5-6pm (NE43-735)
    Greg Shakhnarovich
    Pre-final week:
    Monday 12/2 - office hours 11am-noon, 3-5 pm
    Tuesday 12/3 - review session 5-7pm in NE43-941
    (note that the Lecture on Tuesday takes place as usual)
    Wednesday 12/4 - office hours 11am-noon,4-6pm
    Thursday 12/5 - final in class 2:30-4pm