Methods IV: Statistical Learning

Abstract

This course provides an introduction to methods for supervised and unsupervised learning for the social sciences.

Objective

The goal of this course is provide students with an introduction to statistical learning methods. Upon completion of the course, students will have an understanding of modern computational methods for statistical modelling and prediction, the assumptions on which they are based, and be able to use them to address specific research questions in the social sciences.

Content

Topics include logistic regression and classification, resampling methods, shrinkage approaches and regularization, tree-based methods, support vector machines, double/debiased machine learning for causal inference, and unsupervised learning for natural language processing.

Resources