High Dimensional Stat Inferenc. 3 credits, 3 contact hours

Catalog description

Prerequisite: MATH 665 or permission by instructor. This course introduces modern statistical inference theory and methods developed as a result of the influence of computing. The course covers statistical thinking, ideas and theory that underlie many of the statistical learning algorithms used in data science, such as bootstrap, EM algorithm, cross-validation, large-scale hypothesis test, false discovery rates, sparse modeling, support vector machines and ensemble learning.