CS 540
CS 540 - Deep Learning Theory
Fall 2022
| Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|---|
| Deep Learning Theory | CS540 | DLT | 75414 | LCD | 4 | 1230 - 1345 | T R | 2055 Sidney Lu Mech Engr Bldg | Matus Jan Telgarsky |
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Official Description
A rigorous mathematical course covering foundational analyses of the approximation, optimization, and generalization properties of Deep Neural Networks. Topics include: constructive and non-constructive approximations with one hidden layer; benefits of depth; optimization in the NTK regime; maximum margin optimization outside the NTK regime; Rademacher complexity, VC dimensino, and covering number bounds for ReLU networks. Evaluation is primarily based on homeworks, with a smaller project component. The course goal is to prepare students perform their own research in the field. Course Information: 4 graduate hours. No professional credit. Prerequisite: Basic linear algebra, probability, proof-writing, and statistics required. Real analysis recommended.