MATH 4685 - Statistical Learning I

(3) First course in a two course sequence designed to introduce students to a set of tools used in the modeling and understanding of big data. The two part course introduces statistical methods in the context of developments in computer science. Emphasis is on application to real data sets. Topics include linear regression, shrinkage methods, classification algorithms, principle component analysis, support vector algorithms and clustering methods. PREREQUISITE: MATH 4635 with a minimum grade of C-.