Please note: This course will be taught in hybrid mode. Hybrid delivery of courses will include synchronous live sessions during which on campus and online students will be taught simultaneously.

Marco Steenbergen has been a professor of political methodology at the University of Zurich since 2011. Prior to that, he held appointments at the University of Bern, the University of North Carolina at Chapel Hill, and Carnegie Melon University. Marco’s research covers methodology, as well as political psychology. He has published several books and articles in these areas. His current research focuses on electoral consideration sets, cleavages and identities, and new forms of political participation.

Course content

This course introduces methods of machine learning for social scientists. The broad objective of machine learning is to uncover patterns in data, either as an exploratory device or to make predictions. The course covers a variety of topics, including supervised, unsupervised, and ensemble learning. We discuss how the general principles of machine learning, as well as specific algorithms. The choice of technique, as well as application and interpretation take center stage in the course. Specific algorithms that will be dis-cussed include artificial neural networks, bagging, boosting, classification and regression trees, clustering, decision rules, k-nearest neighbors, principal components, probabilistic learning, random forests, regression, and support vector machines. General principles include cross-validation, global and local interpretation, loss functions, optimization, regularization, variable importance, and feature selection.

Course Objectives

Machine learning is of ever greater importance in the social sciences, both inside and outside of academia. The ultimate goal of this course is to make you conversant with the most important techniques and ideas of machine learning. This means that you have a good overview of the fields and its relevance for social scientific research. It also means that you have sufficient background knowledge to allow you to study further. This is important because 2-week course can only scratch the surface of machine learning, which evolves quickly. Being conversant with machine learning also means that you understand how to implement these methods, which we shall do in R. Note that the examples will be relatively small, with an eye on minimizing computation time. Where necessary, we shall discuss how to engage in big data analysis.

Course Prerequisites

This is an introductory course, meaning that prior familiarity with machine learning is not expected.  It is useful if you have used the linear regression model before, as it is a starting point for much of the course. A basic knowledge of probability theory is indispensable, as is a working understanding of R. In R, you should know: (1) how to access various data sources; (2) the basic objects of the language; (3) basic operations; (4) the ability to compute descriptive statistics and create graphs; and (5) the basics of tidyverse.

Required text – (this text will be provided by ESS):

Max Kun and Juli Silge, Tidy Modeling with R: A Framework for Modeling in the Tidyverse. ISBN: 978-1492096481.

Background texts

For a cursory introduction to many of the topics, you might consult: Lantz, Brett. 2019. Machine Learning with R: Expert Techniques for Predictive Modeling. Packt Publishing, 3rd. edition.

For an introduction to statistical concepts and R, you might want to consult Learning Statistics with R.

Background knowledge required


Calculus = moderate

Linear Regression = moderate


OLS = elementary

Maximum Likelihood – elementary

Computer Background

R = moderate

For participation in this course, students are required to bring with them their own laptops.


This course is a comprehensive overview of machine learning with a focus on the social sciences. Machine learning comes in several flavours, including (1) unsupervised machine learning; (2) supervised machine learning; and (3) ensemble learning. In this course, we shall touch on all those aspects. The general goal is to give you a broad overview of the field and a strong sense of how these powerful techniques can be put to good social scientific use.

The course interchanges lecture and exercise, so that there is never much time between the explanation of a concept or technique and you using it. We shall do all programming in R. While many courses of this kind focus heavily on predictive performance, we place a great deal of emphasis on interpretation. A socially responsible use of predictive learning requires that we can assess and explain the predictions that are being made, even in highly complex algorithms. Great strides have been made in this area and we shall cover them extensively.


At the end of the course, you should have mastered the following milestones:

  1. Understand the different uses of machine learning in the social sciences.
  2. Be aware of machine learning errors and their remedies.
  3. Know how to engage in cross-validation, including the ability to select appropriate methods.
  4. Know how to interpret machine learning outputs, including global and local interpretations, as well as variable importance.
  5. Understand a variety of machine learning algorithms in detail, including their implementation in R.


You should install R and RStudio on your computer. Details will be provided on the 1st day of the course.





Day 1

What Is Machine Learning?

James et al.,

Chapters 1 & 2

To know your data is always the first step!

Day 2

Unsupervised Machine Learning

James et al.,

Chapter 10

Hierarchical cluster analysis, and K-means clustering.

Day 3

A First Foray into Supervised

Machine Learning

James et al.,

Chapters 3 & 5

Regression analysis, stochastic gradient descent, resampling, and effect plots.

Day 4

Preventing the Dreadful Overfit

James et al.,

Chapter 6

Regularization, principal component regression, partial least squares, and model selection.

Day 5

The World Is Not (Always) Linear

James et al.,

Chapter 7

Polynomials, splines, and generalized additive models.

Day 6-7


James et al.,

Chapter 4

Nearest neighbors, rule-based algorithms, logistic regression, naïve Bayes, and discriminant analysis.

Day 8

Support Vector Machines

James et al.,

Chapter 9

Maximum margin classifiers, soft margins, and support vector regression.

Day 9

An Introduction to Neural Networks

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Cambridge: MIT Press. Chapter 6.

Feed forward networks; regularization in ANNs.

Day 10

Tree-based Algorithms and

Ensemble Learners

James et al.,

Chapter 8.

Classification and regression trees, bagging, boosting, stacking, random forests, and Boruta.