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 neural networks. Students taking graduate version complete additional assignments. Meets with 6.862 when offered concurrently. Recommended prerequisites: 6.006 and 18.06. Enrollment may be limited; no listeners.

This class has 18.02, 6.00, and 6.01 as prerequisites.

6.036 will be offered this semester (Spring 2019). It is instructed by R. Barzilay, T. Jaakkola and L. P. Kaelbling.

Lecture occurs 9:30 AM to 11:00 AM on Tuesdays in 26-100.

This class counts for a total of 12 credits.

You can find more information at the 6.036 Spring 2018 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 Spring 2018

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