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.
6.036 will be offered this semester (Spring 2018). It is instructed by T. S. Jaakkola.
Lecture occurs 11:00 AM to 12:30 PM on Tuesdays and Thursdays in 34-501.
This class counts for a total of 12 credits.
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