Machine Learning & AI for Social Science

In the first half of the course, students will learn a variety of machine learning (ML) and artificial intelligence (AI) models, ranging from regularized regression to random forest, deep learning, and foundation models. In the second half, students will learn how to use such ML and AI methods for the social sciences, e.g., how to use LLMs for text analyses, and how to use flexible ML models for causal inference. Students will collaborate to present discussion papers throughout the semester. The main goal of this course is to help students write a final paper that applies advanced ML and AI methods to social science questions. This course builds on the materials covered in POLS 4700, 4720, 4722, or their equivalent courses (i.e., probability, statistics, linear regression, logistic regression, causal inference with observational and experimental data, and knowledge of statistical computing environment R).