Before course starts

• Read Computational thinking and social science - read chapter 3
• Mandatory pre-exam (Python/R) to check students knowledge

Lecture 1: Supervised ML practices

Lecture 2: Supervised ML algorithms

• Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–215. https://doi.org/10.1214/ss/1009213726
• Zong, X., Meng, X., Silventoinen, K., Nelimarkka, M., & Martikainen, P. (2025). Heterogeneous associations between early-life religious upbringing and late-life health: Evidence from a machine learning approach. Social Science and Medicine, 380(September 2024), 118210. https://doi.org/10.1016/j.socscimed.2025.118210

Additional reading

• Childhood living conditions as predictors of self-rated health status in middle-aged and older adults: Evidence from a machine learning analysis in 27 high-income countries

Lecture 3: Unsupervised ML practices

• Charlie Rahal: On the Responsible use of Pseudo-Random Number Generators in Scientific Research https://crahal.com/files/seeds.pdf
• Responsible use of pseudo-random number generators in research

Lecture 4: Unsupervised ML algorithms

• Nelimarkka, M., & Hellas, A. (2018). Social Help-seeking Strategies in a Programming MOOC. Proceedings of the 49th ACM Technical Symposium on Computer Science Education - SIGCSE ’18 , 116–121. https://doi.org/10.1145/3159450.3159495

Lecture 5: Validation practices, leakage mistakes and Theory

• Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B. J., Jahani, E., Kashyap, R., Kirchner, A., McKay, S., … McLanahan, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences , 117(15), 8398–8403. https://doi.org/10.1073/pnas.1915006117
• McKay, S. (2019). When 4 ≈ 10,000: The Power of Social Science Knowledge in Predictive Performance. Socius: Sociological Research for a Dynamic World , 5 , 237802311881177. https://doi.org/10.1177/2378023118811774
• Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns , 4(9), 100804. https://doi.org/10.1016/j.patter.2023.100804

Lecture 6: Decoder-encoder approaches and other data mining techniques

• Jurek, S. J., & Scime, A. (2014). Achieving Democratic Leadership: A Data-Mined Prescription. Social Science Quarterly , 95(1), 97–110. https://doi.org/10.1111/ssqu.12035
• Verhagen, M. D., Stroebl, B., Liu, T., Liu, L. T., & Salganik, M. J. (2025). The Book of Life approach: Enabling richness and scale for life course research. arXiv preprint arXiv:2507.03027https://arxiv.org/abs/2507.03027

Lecture 7: Revision