Intro to Statistical Learning

Catalog description

Statistical learning refers to a set of tools for modeling and understanding complex datasets. This course covers such topics as regression, classification, resampling, model selection, regularization, tree-based methods, support vector machine, principal components analysis and clustering methods. It concentrates more on the applications of the methods and provides students with valuable hands-on experience. Prerequisites: C or better in each of the following: (1) MATH 304 (linear algebra) or equivalent; (2) one of MATH 329, MATH 445, MATH 446, DIDA 325, or a similar computing course approved by the Director of Undergraduate Studies (DUS); (3) MATH 448 (inferential statistics and simple linear regression) or equivalent; or consent of instructor. Offered regularly.