This module covers key state of the art techniques in statistical learning/machine learning. The emphasis of the course is on techniques from supervised learning in the context of regression modeling. The following content is covered: Fundamental concepts (bias-variance trade-off, curse of dimensionality, flexibility vs. interpretability, resampling techniques), key building blocks (parametric polynomials, spline-regression, tree-based modeling), and frequently employed algorithms (lasso, backfitting, random forest, boosting). Prediction and inference are discussed. Selected applications are used to motivate the different algorithms.