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75709-01 - Practical course: Introduction to Machine Learning in Python (3 CP)
| Semester | spring semester 2025 |
| 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 is heavily recommended. A basic understanding of inferential statistics is also preferred. Participants can bring their own data or use the data that will be provided. |
| 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 | Programming with Python for Social Scientists, Philip D. Brooker, Published by SAGE, 2020 Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199-231. Molnar, Christoph. “Interpretable machine learning. A Guide for Making Black Box Models Explainable”, 2019. https://christophm.github.io/interpretable-ml-book/ |
| Comments | This exercise relates to lecture 74922 and is recommended to be attended together with it. 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 | Tuesday | 12.15-14.00 | Bernoullistrasse 14/16, Seminarraum 02.004 |
Dates
| Modules |
Modul: Humanities and Social Science Coding (Master's degree subject: Digital Humanities) |
| 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 |