MATH 8334 Machine Learning
This course is concerned with advanced concepts of Machine Learning. Topics include but are not limited to Rademacher complexity and VC dimension, model selection, kernel methods, on-line and off-line learning, directed graphical models/Bayes net, latent linear models, Hidden Markov chains, undirected graphical models/Markov random fields, inference and structural learning for graphical models, and maximum entropy models.
Prerequisite
Consent of instructor.
Offered
As scheduled