Statistical Learning.

Catalog description

Students learn essential modeling and prediction techniques and toolsets for classical and modern statistical learning and concentrate on their applications to statistical modeling and prediction problems. Particularly, students implement statistical learning models using well-established statistical software packages and tools in R, Python, and MATLAB, and analyze patterns and information discovered from target data. Topics may include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and recent research trends. Prerequisite: Approval by the Graduate Advisor.