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. 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.
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 http://www.google.com/search?&q=MIT+%2B+6.036&btnG=Google+Search&inurl=https site or on the 6.036 Stellar site.
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