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78301-01 - Lecture: Introduction to Machine Learning in Python for HSS Research (2 CP)
| Semester | spring semester 2026 |
| Course frequency | Irregular |
| Lecturers | Rosa Lavelle-Hill (rosa.lavelle-hill@unibas.ch, Assessor) |
| Content | This course will introduce the basics of big data and machine learning and how it can be used in the context of social science and humanities research. No prior knowledge of machine learning is assumed. The course will cover different topics including how big data methods differ from inferential statistics, data cleaning and pre-processing, different machine learning models, eXplainable AI (XAI) methods, as well as bias and responsible AI. Core machine learning principles such as cross-validation, out-of-sample prediction, and hyper-parameter tuning will be introduced. A key focus will be on interpretable machine learning models such as regression-based models, decision trees, and random forests, as well as different XAI methods such as permutation importance and SHAP. In the exercise sessions, you will receive exercises about the topic of the day's lecture. You will be working on the exercises during the session and will receive tips and feedback. Throughout the course, students will code their own machine learning analysis in Python bit by bit. Therefore, some prior experience with Python and a basic understanding of inferential statistics is heavily recommended. There will only be a few sessions dedicated to recapping on Python and statistics at the beginning before delving into machine learning. For the final project, participants can bring their own data to use. |
| Learning objectives | To think critically about when Big Data and machine learning should be used for social science and humanities research by assessing the advantages, disadvantages, and limitations. To be able to perform a machine learning analysis in Python to answer a specific research question in the social sciences or humanities. |
| Bibliography | Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199-231. https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.pdf Shmueli, G. (2010). To explain or to predict?. Statistical science, 289-310. https://www.jstor.org/stable/pdf/41058949 Lavelle-Hill, R., Smith, G., & Murayama, K. (2025). Bridging traditional-statistics and machine-learning approaches in psychology: Navigating small samples, measurement error, non-independent observations, and missing data. Advances in Methods and Practices in Psychological Science, 8(3), 25152459251345696. https://journals.sagepub.com/doi/pdf/10.1177/25152459251345696 Lavelle-Hill, R., Smith, G., Deininger, H., & Murayama, K. (2025). An explainable artificial intelligence handbook for psychologists: Methods, opportunities, and challenges. Psychological Methods. https://psycnet.apa.org/fulltext/2026-46377-001.pdf Additional: Programming with Python for Social Scientists, Philip D. Brooker, Published by SAGE, 2020 Molnar, Christoph. “Interpretable machine learning. A Guide for Making Black Box Models Explainable”, 2019. https://christophm.github.io/interpretable-ml-book/. |
| Comments | This course has a learning-by-doing format. Students must bring their own laptops to the course. Only three absences are permitted; medical attestation is required for further absences! |
| Admission requirements | The number of participants is limited. In case of over-subscription, students of Digital Humanities will be given priority. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Thursday | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
Dates
| Date | Time | Room |
|---|---|---|
| Thursday 26.02.2026 | 10.15-12.00 | Fasnachtsferien |
| Thursday 05.03.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 12.03.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 19.03.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 26.03.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 02.04.2026 | 10.15-12.00 | Ostern |
| Thursday 09.04.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 16.04.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 23.04.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 30.04.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 07.05.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 14.05.2026 | 10.15-12.00 | Auffahrt |
| Thursday 21.05.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Thursday 28.05.2026 | 10.15-12.00 | Bernoullistrasse 30/32, kleiner Hörsaal 120 |
| Modules |
Doktorat Digital Humanities: Empfehlungen (PhD subject: Digital Humanities) Modul: Humanities and Social Science Coding (Master's degree subject: Digital Humanities) Module: Research and Extension (Master's degree program: Language and Communication) Module: Societal Approaches (Master's Studies: European Global Studies) |
| Assessment format | continuous assessment |
| Assessment registration/deregistration | Reg.: course registration; dereg.: not required |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Humanities and Social Sciences, studadmin-philhist@unibas.ch |
| Offered by | Digital Humanities Lab |