DATA MINING.

Catalog description

The course focuses on topics including but not limited to: linear methods in regression, linear methods in classification, model assessment and selection, regularized models, splines, generalized additive models, model averaging, ensemble learning, support vector machines, neural networks, probabilistic graphical models, cluster analysis, dimension reduction techniques, and multidimensional scaling. Prerequisite: MATH 5305 / STATS 5305 or MATH 5312 / STATS 5312 or permission of instructor. Basic programming skills are preferred.