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Searched for: "6.867" Subjects offered any term 1 subject found.
6.867 Machine Learning
(
)
Prereq: 6.041B or 18.600, 18.06
Units: 3-0-9
Lecture: TR2.30-4 (26-100) Recitation: F9 (26-322) or F11 (26-322) or F12 (26-322) or F1 (26-322) or F2 (26-322) or F3 (26-322) or F4 (26-322) or F9 (36-144) or F10 (36-144) or F11 (36-144) or F12 (56-154) or F1 (56-154) or F2 (56-154) or F3 (56-154) or F4 (56-154) or F10 (26-322)
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
Textbooks (Fall 2017)
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