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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
Literature
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An introduction to statistical learning. Springer 2021, Second Edition. The PDF of the textbook is made freely and legally available by the authors and Springer press and part of the course package.