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 instructed by D. Shah.

This class counts for a total of 12 credits. This is a graduate-level class.

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MIT 6.867 Machine Learning Related Textbooks

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