The objective of this course is to train students to be insightful users of modern machine learning methods. The class covers regularization methods for regression and classification, as well as large-scale approaches to inference and testing. In order to have greater flexibility when analyzing datasets, both frequentist and Bayesian methods are investigated.

Typical applications of the methods presented in this course include, but are not limited to: predicting restaurants’ sanitation inspection scores, uncovering the determinants of recidivism, testing for judges’ impartiality, and carrying out regression analysis and model selection using surveys with very many variables, such as the Current Population Survey.