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78303-01 - Übung: Introduction to Machine Learning in Python for HSS Research (3 KP)
| Semester | Frühjahrsemester 2026 |
| Angebotsmuster | unregelmässig |
| Dozierende | Rosa Lavelle-Hill (rosa.lavelle-hill@unibas.ch, BeurteilerIn) |
| Inhalt | 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. |
| Teilnahmevoraussetzungen | The number of participants is limited. In case of over-subscription, students of Digital Humanities will be given priority. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| wöchentlich | Donnerstag | 12.15-14.00 | Kollegienhaus, Hörsaal 119 |
Einzeltermine
| Datum | Zeit | Raum |
|---|---|---|
| Donnerstag 26.02.2026 | 12.15-14.00 Uhr | Fasnachtsferien |
| Donnerstag 05.03.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 12.03.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 19.03.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 26.03.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 02.04.2026 | 12.15-14.00 Uhr | Ostern |
| Donnerstag 09.04.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 16.04.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 23.04.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 30.04.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 07.05.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 14.05.2026 | 12.15-14.00 Uhr | Auffahrt |
| Donnerstag 21.05.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Donnerstag 28.05.2026 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Module |
Doktorat Digital Humanities: Empfehlungen (Promotionsfach: Digital Humanities) Modul: Forschungspraxis und Vertiefung (Master Studiengang: Sprache und Kommunikation) Modul: Humanities and Social Science Coding (Master Studienfach: Digital Humanities) Modul: Methoden der Gesellschaftswissenschaften (Masterstudium: European Global Studies) |
| Prüfung | Lehrveranst.-begleitend |
| An-/Abmeldung zur Prüfung | Anmelden: Belegen; Abmelden: nicht erforderlich |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | Pass / Fail |
| Belegen bei Nichtbestehen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Historische Fakultät, studadmin-philhist@unibas.ch |
| Anbietende Organisationseinheit | Digital Humanities Lab |