CSCI-B 555 MACHINE LEARNING (3 CR.)
Theory and practice of constructing algorithms that learn functions and choose optimal decisions from data and knowledge. Topics include: mathematical/probabilistic foundations, MAP classification/regression, linear and logistic regression, neural networks, support vector machines, Bayesian networks, tree models, committee machines, kernel functions, EM, density estimation, accuracy estimation, normalization, model selection.
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|LEC||3||9325||Closed||4:00 p.m.–5:15 p.m.||MW||GA 1112||Khardon R
Regular Academic Session / In Person
LEC 9325: Total Seats: 60 / Available: 0 / Waitlisted: 28Show Details for section 9325