Introduction to modern statistical machine learning, in particular probabilistic models. Syllabus: • Hands-on Introduction • Bayesian Decision Theory, Generative Probabilistic Models • Discriminative Probabilistic Models • Maximum Margin Classifiers, Generalized Linear Models • Estimators; Overfitting/Underfitting, Regularization, Model Selection • Bias/Fairness, Domain Adaptation • Statistical Learning Theory • Deep Learning