6.862 Applied Machine Learning (New)
Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; clustering, classification, probabilistic modeling; and methods such as support vector machines, hidden Markov models, and Bayesian networks. Students taking graduate version complete different assignments.
This class has no prerequisites.
This class counts for a total of 12 credits. This is a graduate-level class.
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