6.036 Introduction to Machine Learning
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.
This class has 6.01 as a prerequisite.
Lecture occurs 2:30 PM to 4:00 PM on Tuesdays and Thursdays in 26-100.
This class counts for a total of 12 credits.
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