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.
6.867 will not be offered this semester. It will be available in the Fall semester, and will be instructed by D. Shah.
This class counts for a total of 12 credits. This is a graduate-level class.
You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.867&btnG=Google+Search&inurl=https site or on the 6.867 Stellar site.
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