MATH 6685 - 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, principle component analysis, support vector algorithms, clustering methods, and ethical aspects of statistical learning. PREREQUISITE(S): MATH 4/6614 with a minimum grade of C-, or MATH 4/6635 with a minimum grade of C-, or with permission of instructor.