Only for MA Comparative and International Studies.
Introduction to methods for supervised and unsupervised learning for the social sciences.
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 computiational methods for 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.
Topics include linear regression with interaction and fixed effects, binary logistic regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, principal component analysis, factor analysis, and item response theory.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An introduction to statistical learning. Springer, 2013. (7th edition).
The PDF of the textbook is made freely and legally available by the authors and Springer press and part of the course package.
Prerequisites / Notice
Performance assessment information (valid until the course unit is held again)