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.

Pre-requisites: Quantitative Analysis II, Microeconomics, and an introductory computer science course (DSPC IA6000 or equiv). Students who have attained mastery of the prerequisite concepts through other means may petition for an exception to the prerequisites using the form: https://bit.ly/applyingMLpetition