UB - University at Buffalo, The State University of New York

Introduction to Machine Learning

Involves teaching computer programs to improve their performance through guided training and unguided experience. Takes both symbolic and numerical approaches. Topics include concept learning, decision trees, neural nets, latent variable models, probabilistic inference, time series models, Bayesian learning, sampling methods, computational learning theory, support vector machines, and reinforcement learning.

http://www.cedar.buffalo.edu/~srihari/CSE574/index.html

CSE 250 and any of EAS 305/308, STA 401/421, MTH 309; or permission of instructor.