Course # 41430 Section Number 2 Day(s) Tu- Th Time(s) 5:00pm-6:20pm Term Fall 2024 Syllabus Syllabus 9/18/24 This course explores how modern developments in machine learning, AI and statistical learning may be leveraged in order to improve regression analysis and reduced-form econometrics more generally. For instance, we provide a modern, optimization-conscious treatment of linear regression, quantile regression, instrumental variables, and Fréchet-Hoeffding bounds. Throughout, we make an effort to produce methods that remain valid even when models are --as they always are-- misspecified. To produce such regression methods, we fetch tools and results from fields such as linear programming, optimal transport, numerical linear algebra, deep learning and reinforcement learning. Quarter Title Instructor Day(s) Time(s) Syllabus Fall 2024 Modern Methods for Applied Regression Tuesday, Thursday 3:30pm-4:50pm Syllabus Fall 2024 Modern Methods for Applied Regression Tuesday, Thursday 5:00pm-6:20pm Syllabus Recent News More news University of Chicago’s Harris School of Public Policy and Institute for Climate and Sustainable Growth Launch New Pathbreaking Master’s Program in Climate and Energy Policy Tue., July 08, 2025 Provost Katherine Baicker Appointed Emmett Dedmon Distinguished Service Professor Thu., July 03, 2025 Alumni Profile: Daisuke Kageyama, MPP'23 Thu., June 26, 2025 Upcoming Events More events Get to Know Harris! A Virtual Information Session Wed., July 09, 2025 | 8:30 AM Harris Alumni Roundtables in Washington, DC: Transitions between public and private sector roles Wed., July 09, 2025 | 8:30 AM Office of Federal Relations 1730 Pennsylvania Ave NW Washington, DC 20006 United States Harris Summer Mixer in Washington, DC: Cultivating Policy Connections Thu., July 10, 2025 | 5:00 PM Harris Summer Mixer in Washington, DC: Cultivating Policy Connections Office of Federal Relations 1730 Pennsylvania Ave NW Washington,, DC 20006 United States