Supervised learning methods for regression and classification. Basics of machine learning; linear regression and extensions; logistic regression, generative classification models and generalized linear models; cross-validation and bootstrap; model selection and regularization, including subset selection, ridge and lasso, as well as dimensional reduction methods; polynomial and spline regression, generalized additive models; decision tree methods, including bagging, random forests, boosting and Bayesian decision trees; support vector machines; deep learning, including convolutional and recurrent neural networks, as well as practical implementation aspects. Extra topics include unsupervised learning; conformal prediction and multiple testing. The course will have a computational component in Python.