Course: STAT 37710=CAAM 37710, CMSC 35400
Title: Machine Learning
Instructor(s): Rebecca Willett
Teaching Assistant(s): TBA
Class Schedule: Sec 01: MW 1:30 PM–2:50 PM in Eckhart 133
Textbook(s): Bishop, Pattern Recognition and Machine Learning (Optional suplementary materials: Duda, Hart, and Stork, Pattern Classification; Shalev-Schwartz ad Ben-David, Understanding Machine Learning)
Description: This course provides hands-on experience with a range of contemporary machine learning algorithms, as well as an introduction to the theoretical aspects of the subject. Topics covered include: the PAC framework, elements of computational learning theory, the VC dimension, boosting, Bayesian learning, graphical models, clustering, dimensionality reduction, linear classifiers, kernel methods including SVMs, and an introduction to statistical learning theory.