Introduction to Machine Learning for Social Sciences

This course is offered as part of the Radboud Summer School in Social Research Methods, in collaboration with MethodsNET (a global network that offers excellent training in social research methods).

Machine Learning is an approach where statistical models “learn” from data to make accurate predictions and decisions. In this course participants will learn the fundamentals of machine learning as a data analysis appro

Course leader

Bruno Castanho Silva
Researcher
Cologne Center for Comparative Politics
University of Cologne

Target group

-Master
-PhD
-Post-doc
-Professional

This PhD level course is open to all researchers aiming at bringing their research to the next level. It is particularly designed for those who have some familiarity with statistics and want to be on top of the most recent developments in quantitative social sciences.

Course aim

After this course you are able to:

-Learn to define the prediction problem you are interested in
-Identify the appropriate method to approach it
-Fit and interpret different types of machine learning models
-Critically read and understand published research using machine learning

Credits info

2 EC
2 ECTS credits, with the possibility of an extra 1-3 ECTS credits depending on additional course work and assignments handed in during or after the summer school (for a possible total of up to 5 ECTS).

Fee info

EUR 575: The fee includes the registration fees, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities.

Scholarships

We offer several reduced fees:
€ 518 early bird discount- deadline 1 April 2022 (10%)
€ 489 partner + RU discount (15%)
€ 431 early bird + partner + RU discount (25%)