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.

Credits

3

Prerequisite

Consent of instructor.

Schedule Type

Lecture

Grading Basis

Standard Letter (A-F)

Administrative Unit

School of Mathematical & Stat

Offered

As scheduled