Machine Learning

From self-driving cars to your phone's autocorrect, machine learning algorithms are ubiquitous in a big data world. Their potential is now being explored in the social sciences. In this workshop participants will learn the fundamentals of machine learning as a data analysis approach, and will have an overview of the most common and versatile classes of supervised learning techniques for social scientists. The goal is that at the end participants will be able to identify what kind of technique is more suitable for their question and data, and how to design, test and interpret their models. They will also be equipped with sufficient basic knowledge to proceed independently for more advanced algorithms and problems. This is an introductory course, so that math and programming technicalities will be kept to a minimum.

Participants should have some familiarity with quantitative analysis/statistics (i.e., they should know how to run a linear and a logistic regression), and they should be at least a bit familiar with R (reading and manipulating data and running regressions).

Dates

The course will take place in person at the GIGA in Hamburg on the 10th and 11th June 2024, both days from 10 am to 5 pm. Please note that the course is open for external participants.

About the lecturer

Bruno Castanho Silvais an assistant professor for Methods in Empirical Social Sciences at the Freie Universität Berlin. He holds a PhD in Political Science from the Central European University in Budapest, Hungary, and has taught dozens of courses and workshops on introductory and advanced social science quantitative methods, including Machine Learning, Text Analysis, Causal Inference, and Multilevel Structural Equation Modeling. His substantive work focuses on political behavior, legislative politics, and social media in politics, and has appeared in the American Political Science Review, American Journal of Political Science, Political Analysis, and The Journal of Politics, among others.