Machine Learning: Applications in Social Science Research


  • Christopher Hare, University of California at Davis

A growing number of social scientists are taking advantage of machine learning methods to uncover hidden structure in their data, improve model predictive power, and gain a better understanding of complex relationships between variables. This workshops covers the mechanics underlying machine learning methods and discusses how these techniques can be leveraged by social scientists to gain new insight from their data. Specifically, the workshop will cover both supervised and unsupervised methods: decision trees, random forests, boosting, support vector machines, neural networks, deep learning, adversarial methods, ensemble learning, principal components analysis, factor analysis, and manifold learning/multidimensional scaling. We will also discuss best practices in fitting and interpreting these models, including cross-validation techniques, bootstrapping, and presenting output. The workshop will demonstrate how these models can be estimated in R (and, time permitting, Python).

Fees: Consult the fee structure.

Tags: machine learning, social science

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): June 24 - July 19

Time: 1:00 PM - 3:00 PM


  • Christopher Hare, University of California at Davis