6.867 Machine Learning
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.
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.
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