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Graduate

 

Computer Science

B555 Machine Learning

Credits: 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.

Spring 2014

Instructor: Predrag Radivojac
Time/Day: 1:00PM-2:15PM Mon, Wed
Location: Informatics East, Room 130
Links: Homepage

Total SeatsAvailable SeatsWaitlisted
5023