**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 – (t****his text will be provided by ESS):**

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. *An Introduction to Statistical Learning with Applications in R.* New York: Springer. ISBN: 978-1-4614-7138-7.

**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, 3^{rd}. edition.

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

**Background knowledge required**

**Maths**

Calculus = elementary

Linear Regression = elementary

*Statistics*

OLS = elementary

Maximum Likelihood – elementary

*Computer Background*

R = moderate

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