Johns Hopkins University 3 credits Computer Science
This machine learning course will provide a formal and in-depth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in large-scale machine- learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. Prerequisite(s): (AS.110.202 OR AS.110.211) AND (((EN.553.420 OR EN.553.421) AND (EN.553.430 OR EN.553.431)) OR (EN.553.211 OR EN.553.310 OR EN.553.311) AND (AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295)) AND (EN.500.112 OR EN.500.113 OR EN.500.114) OR (EN.601.220 OR AS.250.205 OR EN.580.200 OR EN.601.107)
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