Machine Learning: Applications in Social Science Research

Instructor(s):

  • Christopher Hare, University of California at Davis

This workshop will be offered in an online video format.

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).

Fee and Registration: This course is part of the first four-week session. Please see our fee chart on our Registration page for the cost of attending one (or both) four-week session(s). Participants who enroll in a four-week session may take as many courses (workshops and lectures) as desired during the session for which they are enrolled. Participants in the four-week sessions are also welcome to attend all of the lectures and discussions offered in the Blalock Lecture Series.

Tags: machine learning, social science

Course Sections

Section 1

Location: Online -- Video format,

Date(s): June 22 - July 17

Time: 3:00 PM - 5:00 PM

Instructor(s):

  • Christopher Hare, University of California at Davis