6.867 Machine Learning

Class Info

Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, and Bayesian networks. Recommended prerequisite: 6.036.

This class has 6.041B, 18.600, and 18.06 as prerequisites.

6.867 will not be offered this semester. It will be available in the Fall semester, and will be instructed by L. P. Kaelbling and T. Jaakkola.

Lecture occurs 2:30 PM to 4:00 PM on Tuesdays and Thursdays in 26-100.

This class counts for a total of 12 credits.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.867&btnG=Google+Search&inurl=https site or on the 6.867 Stellar site.

Required Textbooks
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MIT 6.867 Machine Learning Related Textbooks

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