This course covers a number of statistical methods that are commonly referred to as "machine learning." These include methods for both regression and classification. Topics include regularization, including lasso and elastic net; methods based on logistic regression; tree-based methods, including random forests, bagging, and boosting; nonparametric regression, including kernel regression and splines; support vector machines; and neural networks.
Students will be expected to understand the key ideas of the methods that are discussed and to become proficient at using several of them to analyze actual datasets. There will be empirical assignments and a major empirical project.
Please note that non-Economics students who are interested in this course must obtain the Instructor's approval before registering.