Zurück zur Auswahl
54857-01 - Kolloquium: Machine Learning for Economists and Business Analysts (3 KP)
| Semester | Frühjahrsemester 2026 |
| Angebotsmuster | unregelmässig |
| Dozierende | Anthony Strittmatter (anthony.strittmatter@unibas.ch, BeurteilerIn) |
| Inhalt | This course introduces key concepts and methods of machine learning for prediction, causal analysis, and data exploration. It conveys methodological understanding through practical applications using the statistical software R. Supervised learning focuses on predicting an outcome of interest, such as sales, based on observed relationships with explanatory variables like price or quality. The objective is to model these relationships in a data-driven way to improve out-of-sample predictive performance. Unsupervised learning is used to explore data when no specific outcome is defined. It can reveal hidden structures, such as clusters of similar observations or latent factors that summarize complex data. These methods help to better understand and visualize datasets. Causal machine learning aims to estimate how an action, intervention, or policy affects an outcome of interest (e.g. the impact of education on wages). It relies on data-driven approaches to make fair comparisons between those who did and did not receive the intervention and to assess whether effects differ across groups. Such analyses can inform decisions about where a policy or program is likely to have the strongest impact. Reinforcement learning extends these ideas to dynamic settings in which agents learn to make sequential decisions based on feedback from previous actions. It provides a framework for studying adaptive behavior and optimal decision-making under uncertainty. The course discusses the main assumptions, algorithms, and implementation issues associated with these approaches and illustrates their use through applications to empirical data. Topics include: • Model tuning and evaluation (cross-validation and out-of-sample testing) • Penalized regression (lasso and ridge regression) • Tree-based methods (decision trees and random forests) • Unsupervised methods (principal component analysis, clustering) • Causal analysis using penalized regression and tree-based approaches (double machine learning and causal forests) • Analysis of differential policy impacts Lecture slides, R code, and data files will be made available to the course participants. |
| Lernziele | • To understand the ideas, goals, and differences of machine learning for prediction and for causal analysis • To understand the intuition, advantages, and disadvantages of alternative methods • To be able to apply predictive and causal machine learning to real world data using the software R |
| Literatur | G. James, D. Witten, T. Hastie, and R. Tibshirani (2021): An Introduction to Statistical Learning with Applications in R, Springer, New York. Freely available at: https://www.statlearning.com/ Further references will be provided during the course. |
| Teilnahmevoraussetzungen | Basic knowledge of statistics and econometrics (probability theory, conditional means, linear regression). |
| Anmeldung zur Lehrveranstaltung | Registration: Please enroll in the Online Services (services.unibas.ch); Eucor-Students and mobility students of other Swiss Universities or the FHNW first have to register at the University of Basel BEFORE the start of the course and receive their login data by post (e-mail address of the University of Basel). Processing time up to a week! Detailed information can be found here: https://www.unibas.ch/de/Studium/Mobilitaet.html After successful registration you can enroll for the course in the Online Services (services.unibas.ch). Applies to everyone: Enrolment = Registration for the course and the exam! |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| HörerInnen willkommen |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| täglich | Siehe Einzeltermine | ||
Einzeltermine
| Datum | Zeit | Raum |
|---|---|---|
| Mittwoch 11.03.2026 | 08.30-12.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S17 HG.38 |
| Donnerstag 12.03.2026 | 08.30-12.00 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S17 HG.38 |
| Donnerstag 12.03.2026 | 13.00-14.30 Uhr | Wirtschaftswissenschaftliche Fakultät, Seminarraum S17 HG.38 |
| Freitag 13.03.2026 | 10.45-12.15 Uhr | Juristische Fakultät, Seminarraum S5 HG.54 |
| Freitag 13.03.2026 | 13.00-14.30 Uhr | Juristische Fakultät, Seminarraum S5 HG.54 |
| Mittwoch 25.03.2026 | 10.30-14.30 Uhr | In Brig, -- |
| Donnerstag 26.03.2026 | 08.30-14.30 Uhr | In Brig, -- |
| Freitag 27.03.2026 | 08.30-12.00 Uhr | In Brig, -- |
| Module |
Modul: Fachlich-methodische Ausbildung (Promotionsfach: Wirtschaftswissenschaften (Staatswissenschaften)) Modul: Fachlich-methodische Weiterbildung (Doktoratsstudium - Wirtschaftswissenschaftliche Fakultät (Studienbeginn vor 01.02.2024)) |
| Prüfung | Leistungsnachweis |
| Hinweise zur Prüfung | Participation, individual and group home assignments |
| An-/Abmeldung zur Prüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | Pass / Fail |
| Belegen bei Nichtbestehen | beliebig wiederholbar |
| Zuständige Fakultät | Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch |
| Anbietende Organisationseinheit | Wirtschaftswissenschaftliche Fakultät / WWZ |