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 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.
Lecture occurs 9:30 AM to 11:00 AM on Tuesdays in 26-100.
This class counts for a total of 12 credits.
MIT 6.036 Introduction to Machine Learning Related Textbooks
MIT 6.036 Introduction to Machine Learning On The Web
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