Participants will learn how machine learning methods can be used to select covariates through covariate importance of e.g. random forest or boosted trees. Moreover, students will learn that machine learning models are not just black boxes. Interpretation of covariate-response relationships are discussed with partial dependence plots and maps. Finally, uncertainty of model predictions by bootstrapping will be discussed.