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COS 511: 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.


Semester: Fall23
Lectures: Friday, 1:30-4:20
Location: Computer Science 104

Additional Information


The Graduate Coordinator is Nicki Mahler
Email: ngotsis
Office: Computer Science 213
Extension: 5387
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