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 be offered this semester (Fall 2017). It is instructed by D. Shah and D. A. Sontag.

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.

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