Introduction to Statistical Learning With R

Catalog description

An introduction to Statistical Learning with extensive applications with the R programming language. Emphasis on various types of statistical models including Linear and Nonlinear Regression, Regression Trees, Clustering, Classification Trees, and Random Forests. An exploration of advanced techniques including Regularization (Ridge and Lasso), K-Nearest Neighbor, and Principal Component Analysis. Finally, an introduction to model evaluation including Cross-Validation and the Bias-Variance Trade-Off. This course helps to prepare students for the examination "Statistics for Risk Modeling" in the Actuarial Profession. Prerequisites: MAT-322 and one of MAT-363 or MAT-204