Applying Machine Learning

The widespread adoption of information technology has resulted in the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand and improve the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and social science (theory, reduced-form causal inference, structural modeling) to address real-world problems. We will use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

Prerequisites: Students are expected to have completed coursework equivalent to Quantitative Analysis II or Statistics (e.g., SIPA U6501), Microeconomics (e.g., SIPA U6300/50 or U6400), and an introductory Computer Science course (e.g., INAF U6006). Familiarity with econometrics and programming is assumed.