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6.867 Machine Learning, Fall 2002

Image of robotic mannequin, 'Manny,' constructed at Pacific Northwest Laboratory.
Robotic mannequin, "Manny", constructed at Pacific Northwest Laboratory. (Image is taken from Department of Energy's Digital Archive.)

Highlights of this Course

6.867 is offered under the department's "Artificial Intelligence and Applications" concentration. The site offers a full set of lecture notes, homework assignments, in addition to other materials used by students in the course.

Course Description

6.867 is an introductory course on machine learning which provides 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 gives 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.

 

 

Staff

Instructor:
Prof. Tommi Jaakkola

Course Meeting Times

Lectures:
Two sessions / week
1.5 hours / session

Recitations:
Two sessions / week
1 hour / session

Level

Graduate

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