MIT Subject Listing & Schedule
IAP / Spring 2019 Search Results

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6.862 Applied Machine Learning

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Prereq: Permission of instructor
Units: 4-0-8
Credit cannot also be received for 6.036
Lecture: T9.30-11 (26-100) Recitation: T11-12.30 (34-501) or T1-2.30 (34-501) or T2.30-4 (34-501) or R9.30-11 (34-501) or R11-12.30 (34-501) or R1-2.30 (34-501) or R2.30-4 (34-501) +final

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; classification, regression, reinforcement learning; and methods such as linear classifiers, feed-forward, convolutional, and recurrent networks. Students taking graduate version complete different assignments. Meets with 6.036 when offered concurrently. Recommended prerequisites: 18.06 and 6.006. Enrollment limited; no listeners.
S. Jegelka
No textbook information available