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||17011||Open||4:55 p.m.–6:10 p.m.||MW||OP 105||Khardon R