3.0

credits

Average Course Rating

This course provides a broad overview of the different machine learning methods and their theoretical foundations. We focus on the applicability of each method for appropriate statistical design, the interpretability of simple or well-constrained methods, the explainability of complex models or black boxes, and the quantitative characterization of uncertainties. Theoretical and technical aspects related to model evaluation and actionable predictions will be covered, including feature selection, variable importance, model intercomparisons, and cross validation. Applications to real problems in natural sciences and engineering will be covered.