Course # 41430 Section Number 2 Day(s) Tu- Th Time(s) 5:00pm-6:20pm 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 Student Profile: Melaku Lakew, MPP Class of 2027 Thu., May 15, 2025 Chicago’s Sit-D Program: Transforming Police Behavior with Science-Based Training Wed., May 14, 2025 New Research Finds Medicare Soft Spending Cap Lowers Costs without Harming Patient Health, But Introduces Inequities Wed., May 14, 2025 Upcoming Events More events Ask Admissions: Credential Programs Mon., May 19, 2025 | 7:30 PM Ask Admissions: Credential Programs Tue., May 20, 2025 | 7:00 AM UChicago Part-Time Programs Information Session - Harris, Crown, Graham, and UCPE Tue., May 20, 2025 | 12:00 PM