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6.867 Machine Learning

Prereq: 6.041B or 18.600, 18.06
G (Fall)
12 Units

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.

T. Jaakkola, L. P. Kaelbling

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