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 Q&A: Robert Kaestner Discusses the How and Why of Potential Medicaid Cuts Tue., April 01, 2025 Professor Joshua Gottlieb Receives ASHEcon Medal Fri., March 28, 2025 Alumni Profile: Isabel Kennon, MPP’23 Tue., March 25, 2025 Upcoming Events More events Financing your Graduate School Investment: Scholarships & Financial Aid Wed., April 02, 2025 | 8:30 AM Admitted Students Day: Virtual Panel with the Employer Partnerships Team Wed., April 02, 2025 | 10:30 AM Merit or Privilege?: Test Scores and College Admissions Wed., April 02, 2025 | 5:00 PM University of Chicago, Harris School of Public Policy 1307 E. 60th St. The Keller Center CHICAGO, IL 60637 United States
February 24, 2025 Professor Konstantin Sonin Sheds Light on Purges During Joseph Stalin's Great Terror in New Paper