Machine Learning for Social Science

Catalog description

This course provides an introduction to machine learning techniques for social science researchers. The course will cover a range of techniques including supervised and unsupervised learning, as well as more specialized methods such as deep learning and natural language processing. The course will also discuss ethical and privacy considerations in the use of machine learning, as well as the role of machine learning in policy and decision-making. The aim of the course is to be focused on applications. While the class will present the formal background on the development of the machine learning methods, the class will focus on putting the tools into practice. We will use data on a variety of topics including criminal justice data (recidivism prediction) as well as other social science disciplines. Students completing the course will know how to apply several of the most common machine learning tools to a variety of social science problems including prediction and clustering. The course will also discuss the role of machine learning in causal inference.