Statistical Learning with Applications in the R Programming Language

Catalog description

This course introduces supervised learning through hands-on examples in the R programming language. Topics include linear and logistic regression, subset selection, shrinkage methods, principal components, classification and regression trees, ensemble methods, and neural networks. Statistical aspects of model selection, validation, and reproducibility will be emphasized. High-dimensional applications will be highlighted.  This course includes “computational labs,” demonstrating methods and concepts in the R programming language using real data. Cutting-edge databases and packages such as Bioconductor, tidyverse, ggplot2, and plotly will be introduced in connection to workflow design.