Course # 41430 Section Number 1 Day(s) Tu- Th Time(s) 3:30pm-4:50pm 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 Q&A: Assistant Professor Erin Kelley on Her Work in Program Evaluation, International Development, and With the World Bank Wed., July 09, 2025 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 Upcoming Events More events Get to Know Harris! Public Sector Scholarship: A Conversation with Ranjan Daniels Tue., July 15, 2025 | 12:00 PM Civic Leadership Academy 2026 Virtual Information Session Wed., July 16, 2025 | 12:00 PM Bridging Capital and Communities: Integrating Impact Investing, Real Estate Development, and Public Policy Thu., July 17, 2025 | 7:00 PM