Machine Learning for Social Science

Education facts

Code: 771A42

<p>This course provides an overview of the key concepts and tools of machine learning (ML) that are relevant to social science research. First, a general introduction to ML is provided, where foundational ideas are reviewed and contrasted to those of traditional statistics. Then, central techniques in supervised learning (e.g., decision trees) and unsupervised learning (e.g., k-means) are introduced. In computer labs, students learn how to use these techniques in statistical software to solve practical problems relevant for social scientific research. Finally, the intersection between ML and causal inference will be considered.</p>

Entry requirements

180 ECTS credits passed including 90 ECTS credits within one of the following areas humanities, social-, cultural-, behavioural-, natural-, computer-, or engineering-sciences 15 ECTS credits passed in one or several of the following subjects: Statistics Mathematics Computer science English corresponding to the level of English in Swedish upper secondary education (Engelska 6 or Engelska nivå 2) Exemption from Swedish

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Machine Learning for Social Science

  Linköping University

  Start date: 17 August 2026

  End date: 25 October 2026

  Pace of study: 50 %