| Dozent/in |
Prof. Dr. Markus Johannes Meierer |
| Veranstaltungsart |
Vorlesung |
| Code |
HS261043 |
| Semester |
Herbstsemester 2026 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Bachelor |
| Termin/e |
Mo, 07.09.2026, 09:15 - 14:00 Uhr, E.508
Di, 08.09.2026, 09:15 - 15:00 Uhr, E.508
Mi, 09.09.2026, 09:15 - 15:00 Uhr, E.508
Do, 10.09.2026, 09:15 - 15:00 Uhr, E.508 |
| Umfang |
Blockveranstaltung |
| Turnus |
Block course
|
| Inhalt |
Machine learning has become one of the core pillars of business analytics. Since the amount of available data is steadily increasing, applying smart data analysis techniques will become more and more important in the future. This course introduces (supervised) machine learning techniques in a hands-on way with integrated exercises.
The distinction between supervised/unsupervised/reinforcement learning, sampling and cross-validation, performance evaluation, logistic regression, decision trees, random forest, support vector, machines, deep learning, and ensemble methods are among the topics to be discussed in this course. An integral part of this lecture are integrated exercises during which the students will become familiar with setting up machine learning models in the programming language R. |
| Lernziele |
- Get familiar with the concept of (supervised) machine learning.
- Understand the basic theory behind various machine learning techniques.
- Apply different machine learning techniques and interpret the results. |
| Voraussetzungen |
- Bring a laptop (with the latest operating system version installed)
- Updated installation of R (https://cran.r-project.org/)
- Updated installation of RStudio (https://www.rstudio.com/) |
| Sprache |
Englisch |
| Anmeldung |
Binding registration takes place via the UniPortal; see notes below in the «Proof of Performance» field.
For course information and materials, registration on the OLAT e-learning platform is required from 31 August – 6 September 2026. The students themselves are responsible for verifying the course’s creditability towards their degree program.
Direct link to the OLAT course: https://lms.uzh.ch/url/RepositoryEntry/17903616192 |
| Leistungsnachweis |
Daily examinations during the course of the block course.
***IMPORTANT*** To receive course credit and earn academic credits, registration via the Uni Portal from 31 August (starting at 9:00 a.m.) – 6 September 2026 is MANDATORY.
Late registrations and withdrawals will not be accepted. Once the registration period has ended, participation in the course is MANDATORY. If the course is not completed without a valid reason and without proper withdrawal (including supporting documentation; see the Exam Guidelines), the course will be considered failed (grade 1). |
| Abschlussform / Credits |
multiple-choice exams on programming exercises and theory / online exercises / machine learning competition / 3 Credits
|
| Hinweise |
Lecture with integrated exercises (details are announced during the kick-off session on course logistics). |
| Hörer-/innen |
Nein |
| Kontakt |
markus.meierer@doz.unilu.ch |