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Searched for: 1 subject found.
6.7900 Machine Learning
(
)
Prereq: 18.06 and (6.3700, 6.3800, or 18.600)
Units: 3-0-9
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, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.3900 or other previous experience in machine learning. Enrollment may be limited.
T. Broderick
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