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, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.036 or other previous experience in machine learning.
Lecture occurs 2:30 PM to 4:00 PM on Tuesdays and Thursdays in 32-123.
This class counts for a total of 12 credits.
MIT 6.867 Machine Learning Related Textbooks
MIT 6.867 Machine Learning On The Web
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