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.

This class has 6.01 as a prerequisite.

6.036 will not be offered this semester. It will be available in the Spring semester, and will be instructed by R. Barzilay, L. P. Kaelbling and T. Jaakkola.

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.

In the Spring 2016 Subject Evaluations, 6.036 was rated 4.9 out of 7.0. You can find more information at the 6.036 Introduction to Machine Learning site or on the 6.036 Stellar site.

MIT 6.036 Introduction to Machine Learning Related Textbooks
MIT 6.036 Introduction to Machine Learning On The Web
6.036 Introduction to Machine Learning
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