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Searched for: 1 subject found.
6.036 Introduction to Machine Learning
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Prereq: Calculus II (GIR) and (6.0001 or 6.01)
Units: 4-0-8
Credit cannot also be received for 6.862
https://eecs.scripts.mit.edu/eduportal/__How_Courses_Will_Be_Taught_Online_or_Oncampus__/F/2020/#6.036
Lecture: T9.30-11 (VIRTUAL) Recitation: R9.30-11 (VIRTUAL) or R11-12.30 (VIRTUAL) or R1-2.30 (VIRTUAL) or R2.30-4 (VIRTUAL) or F9.30-11 (VIRTUAL) or F11-12.30 (VIRTUAL) or F1-2.30 (VIRTUAL) or F2.30-4 (VIRTUAL)
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.
Fall: D. Boning
Spring: L. Kaelbling
No textbook information available
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