6.867 Machine Learning

Fall 2006

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.)

Course Description

6.867 is an introductory course on machine learning which gives 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.

Recommended Citation

For any use or distribution of these materials, please cite as follows:

Tommi Jaakkola, course materials for 6.867 Machine Learning, Fall 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].

Technical Requirements

Special software is required to use some of the files in this course: .m, .dat, and .zip.


*Some translations represent previous versions of courses.

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Staff

Instructor:
Prof. Tommi Jaakkola

Teaching Assistants:
Ali Mohammad
Rohit Singh

Course Meeting Times

Lectures:
Two sessions / week
1.5 hours / session

Level

Graduate

*Translations

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