Machine Learning for Social Scientists

About

Machine learning is fashionable. But what is it and how can it be put to good use in the social sciences? This introductory course provides an overview of some of the most important machine learning techniques and their social science applications. Those applications can be grouped into several sub-categories:

Preparing data for statistical analysis: Sometimes data are so voluminous that hand-coding them is near-impossible. We can leverage clever computer algorithms to do the coding for us. For example, we could use an artificial neural network to detect if tweets, of which there are millions, come from a social bot or from a legitimate source.
Doing statistical analysis: As social scientists we are used to building models with numerous parametric assumptions. What if we would let algorithms leverage the data to obtain the model for us? That way, we may detect complex contingencies not previously theorized.
Pattern recognition: How do variables hang together and what groups do our cases form in terms of those variables? For example, political parties take positions on numerous issues. Can we group those issues into ideologies? Based on the issues can we place the parties into clusters?
Anomaly detection: Some phenomena such as war are fortunately rare. However, this makes analyzing them challenging. A whole subfield of machine learning is dedicated to the detection of such rare events or anomalies.

Course leader

Prof. Dr. Marco Steenbergen

Target group

All researcher in Social Sciences

Course aim

Machine learning is fashionable. But what is it and how can it be put to good use in the social sciences? This introductory course provides an overview of some of the most important machine learning techniques and their social science applications.

Fee info