Introduction to Statistical Learning. 2 Credit Hours

Catalog description

Machine learning and artificial intelligence (AI) are becoming increasingly common tools for image data analysis and image interpretation. AI methods are also being developed for treatment planning. This short, intensive course is designed to give the student an introduction to the principal methods of statistical learning that underlie artificial intelligence algorithms. Students will learn how to use the R statistical programming language to work through statistical learning exercises both in-class and in homework assignments. Course will be taught 2 hours per day for 3 days per week in July and August. Topics covered will include, Classification Schemes, Resampling Methods, Linear Model Selection and Regularization, Tree-Based Methods, Support Vector Machines, and Unsupervised Learning. Prerequisites: Completion of RADI 5007 , Statistics in the Radiological Sciences; Familiarity with R statistical programming environment. Open for Cross Enrollment on Space Available Basis.