MATH 4685 - Statistical Learning I

Credit Hours: (3)

Description: Introduction to statistical foundations of computational tools for modeling and understanding big data. Emphasis is on application to real data sets. Topics include linear regression, shrinkage methods, classification algorithms, principal component analysis, support vector algorithms, clustering methods, and ethical aspects of statistical learning. PREREQUISITE: MATH 3242 with a minimum grade of C- and either MATH 4614 with a minimum grade of C-, or MATH 4635 with a minimum grade of C-, or with permission of instructor.