Machine Learning for Social Sciences

About

This course provides an introduction to supervised statistical learning techniques such as decision trees, random forests and boosting and discusses their potential application in the social sciences. These methods focus on predicting an outcome Y based on some learned function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. Predictive methods also provide a valuable extension to the empirical social scientists’ toolkit as new (high dimensional) data sources become more prominent. In addition to introducing supervised learning methods, the course will include practical sessions to exemplify how to tune and evaluate prediction models using the statistical programming language R. The course aims to illustrate the covered concepts and methods from a social science perspective by discussing typical applications and social science research problems that may benefit from machine learning tools.

Course leader

Christoph Kern, PhD - University of Mannheim

Target group

Professionals, Researchers, and Students, who wish to develop high-quality surveys and employ up-to-date statistical methods for survey data analysis. Potential attendees include survey practitioners, marketing professionals, social science students, and researchers.