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 Ten Years of MSCAPP: Where Public Policy Meets Coding Wed., July 23, 2025 Rising Star in Mexico: Roberto Velasco Álvarez, MPP'17 Wed., July 23, 2025 Student Profile: Maria Villamizar Londono Wed., July 16, 2025 Upcoming Events More events Harris Summer Campus Visit Mon., July 28, 2025 | 10:00 AM Harris School of Public Policy 1307 E 60th St Chicago, IL 60637 United States Civic Leadership Academy 2026 Virtual Information Session Wed., July 30, 2025 | 12:00 PM UChicago Summer Send-Off in Tokyo Thu., July 31, 2025 | 7:30 PM DevilCraft-Hamamatsucho, Risewell Building, 1F, Minato-ku Tokyo 105-0013 Japan