COS511:Theoretical Machine Learning

Can the mechanism of learning be automated and implemented by a machine? In this course we formally define and study various models that have been proposed for learning. The course presents and contrasts the statistical, computational and game-theoretic models for learning. Likely topics: intro to statistical learning theory and generalization; learning in adversarial settings on-line learning; analysis of convex and nonconvex optimization algorithms, using convex optimization to model and solve learning problems; learning with partial observability; boosting; reinforcement learning and control; introduction to theory of deep learning.

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Semester: Fall24
Lectures: Tuesday,Thursday 1:30 - 3:00
Location: TBD

Faculty

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Elad Hazan
Office: Computer Science 409
Extension: 6031
Email: ehazan

Additional Information

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Registrar's Fall24 COS offerings
CS Course Schedule
The Grad Coordinator is Nicki Mahler.
Email: ngotsis
Office: Computer Science 213
Extension: 5387