6.036 Introduction to Machine Learning

Class Info

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 additional assignments. Meets with 6.862 when offered concurrently. Enrollment may be limited.

This class has 6.0001 as a prerequisite.

6.036 will be offered this semester (Fall 2017). It is instructed by T. S. Jaakkola, L. P. Kaelbling and T. Lozano-Perez.

Lecture occurs 2:30 PM to 4:00 PM on Mondays and Wednesdays in 34-501.

This class counts for a total of 12 credits.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.036&btnG=Google+Search&inurl=https site.

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