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Searched for: 1 subject found.
6.C01 Modeling with Machine Learning: from Algorithms to Applications
(
); first half of term
(Subject meets with 6.C51)
Prereq: Calculus II (GIR) and 6.100A; Coreq: 1.C01, 2.C01, 3.C01, 6.C011, 7.C01, or 22.C01
Units: 2-0-4
Ends Mar 21. Lecture: MW2.30-4 (32-123)
Focuses on modeling with machine learning methods with an eye towards applications in engineering and sciences. Introduction to modern machine learning methods, from supervised to unsupervised models, with an emphasis on newer neural approaches. Emphasis on the understanding of how and why the methods work from the point of view of modeling, and when they are applicable. Using concrete examples, covers formulation of machine learning tasks, adapting and extending methods to given problems, and how the methods can and should be evaluated. Students taking graduate version complete additional assignments. Students cannot receive credit without completion of a 6-unit disciplinary module in the same semester. Enrollment may be limited.
R. Barzilay
No textbook information available
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