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 2013
Instructor: Predrag Radivojac
Time/Day: 1:00PM-2:15PM Tue, Thu
Location: Informatics East, Room 130
Links: Homepage
| Total Seats | Available Seats | Waitlisted |
|---|---|---|
| 50 | 10 | 0 |
