MATH 3190 - Statistical Learning for Data Science

3 Credit(s) | $3.75 Fee

This class will be an introduction to the mathematics and algorithms underlying statistical learning techniques used for data science. Topics covered will include regularization methods, advanced regression, cross validation, Bayesian methods, optimization, dimension reduction, clustering and classification. Database management will also be discussed. Students will use a programming language and GitHub throughout this course. (Spring - Even Years) [Graded Letter]

Prerequisite(s): (MATH 3150 or MATH 3700) and (MATH 2170 or MATH 2270) and a working knowledge of a programming language - Prerequisite Min. Grade: C
Prerequisite Can Be Concurrent? Yes (MATH 2170 or MATH 2270)