ST 473 - Statistical Learning and Big Data

(3.00 cr.)

Covers foundations and recent advances in statistical learning for complex and massive data. Topics include nonlinear regression, smoothing splines, linear/quadratic discriminant analysis, k-nearest neighbors, regression trees, bagging, random forests, boosting, and support vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). Those methods are performed using statistical software - R and SAS.

Prerequisite (may be taken concurrently): ST 310 .
Sessions Typically Offered: Fall
Years Typically Offered: Odd Years

Interdisciplinary Studies: DS