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.
Machine Learning for Social Science
7.5 creditsMachine Learning for Social Science, 7.5 credits
Autumn 2026, Half-time, Norrköping
Closed for late application
Starts:
Autumn 2026
Start date:
17 August
End date:
25 October
Place of study:
Norrköping
Pace of study:
Half-time
Level:
Second cycle
Teaching form:
On-Campus
Education Time:
Day-time
Education Language:
English
Course offering id:
LIU-46034
Number of Places:
7
Specific 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
Type of Selection
Tuition fees
SEK 20800 - NB: Applies only to students from outside the EU, EEA and Switzerland.