Course # 41430 Section Number 1 Day(s) Tu- Th Time(s) 3:30pm-4:50pm Term Fall 2024 Course Instructor Guillaume Pouliot 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 Guillaume Pouliot Tuesday, Thursday 3:30pm-4:50pm Syllabus Fall 2024 Modern Methods for Applied Regression Guillaume Pouliot Tuesday, Thursday 5:00pm-6:20pm Syllabus Recent News More news Alumni Profile: Benjamin Stock, MPP’23 Thu., April 24, 2025 Harris Policy Innovation Challenge Announces 2025 Winning Team Thu., April 17, 2025 Professor Jens Ludwig Analyzes the Use of AI in Econometrics in New Working Paper Wed., April 16, 2025 Upcoming Events More events Get to Know Harris Credential Programs! A Virtual Information Session Tue., April 29, 2025 | 12:00 PM Tariffs, Trade & Tech, Oh My! Gina Raimondo Speaks Tue., April 29, 2025 | 12:30 PM University of Chicago, Harris School of Public Policy 1307 E. 60th St. The Keller Center CHICAGO, IL 60637 United States Data and Policy Summer Program (DPSS) Information Session with Alumni Tue., April 29, 2025 | 7:30 PM