Course # 30545 Term Winter 2025 Specialization Data Analytics The objective of this course is to train students to be insightful users of modern machine-learning methods. The class covers regularization methods for regression and classification, as well as large-scale approaches to inference and testing. In order to have greater flexibility when analyzing datasets, both frequentist and Bayesian methods are investigated. This class is required for the Data Analytic specialization but is open to all students who have taken the Harris core statistics classes (or the equivalent) and have some exposure to programming. Notes Students are required to register for both a lecture (PPHA 30545) and a language-specific discussion (PPHA 30547 in R or PPHA 30548 in Python). Quarter Title Instructor Day(s) Time(s) Syllabus Winter 2024 Machine Learning for Public Policy Jeff Levy Winter 2024 Machine Learning for Public Policy Chris Clapp Recent News More news Natasha Mathur, MSCAPP’19, on the Connection Between the Real World and Data Tue., June 25, 2024 Research Professor Robert Kaestner Assesses Effects of Increased Income on Infant Health Tue., June 18, 2024 Harris Voices: Miguel A. Blancarte, Jr., CLA'21, on Growth During Trying Times Mon., June 17, 2024 Upcoming Events More events Harris Campus Visit Wed., July 10, 2024 | 10:00 AM Keller Center 1307 E 60th St Chicago, IL 60637 United States Harris Evening Master's Program Information Session Wed., July 10, 2024 | 12:00 PM Harris Summer Mixer in Washington, DC: Cultivating Policy Connections Wed., July 10, 2024 | 5:00 PM Office of Federal Relations Rooftop 1730 Pennsylvania Ave NW Washington, DC 20004 United States