Statistical Learning sums up methods from computational statistics that are designed to deal with high dimensional, complex data sets. Various topics that facilitate modeling of and gaining a deeper insight into high dimensional, complex data sets are introduced. Basic subervised and unsupervised statistical learning techniques are presented, discussed, and applied in class (For example hierarchical clustering, linear and nonlinear classification and regression techniques, incorporating lasso, random forests, bagging, boosting, etc.). Meta-parameter selection, model evaluation, and specification choice in practical settings are also covered in the course.