Machine Learning for Public Policy

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. This class is required for the Data Analytic specialization but is open to all students who have taken the Harris core statistics classes (or the equivalent) and have some exposure to programming.