| Machine Learning Theory Smnr - 17240 - CS 59000 - MLT |
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Associated Term: Fall 2015
Levels: Graduate, Professional, Undergraduate West Lafayette Campus Lecture Schedule Type Learning Outcomes: The "Machine Learning Theory" seminar will mainly focus on non-asymptotic analysis of the convergence and statistical efficiency of algorithms. We will introduce several concepts and proof techniques from statistical learning theory, information theory and optimization. The seminar will include topics such as: concentration bounds, empirical risk minimization, PAC-Bayes, Rademacher/Gaussian complexity, Karush-Kuhn-Tucker conditions, primal-dual witness, convergence rates, restricted strong convexity, Fano's inequality. Required Materials: Technical Requirements: Basic knowledge from calculus and linear algebra is required. Some knowledge or experience with machine learning or data mining is welcome. View Catalog Entry
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