| Dozent/in |
Dr. rer. pol. Markus Johannes Meierer; Dr. oec. Patrick Bachmann; |
| Veranstaltungsart |
Vorlesung |
| Code |
HS241115 |
| Semester |
Herbstsemester 2024 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Bachelor |
| Termin/e |
Mo, 09.09.2024, 09:15 - 15:00 Uhr, 4.B47
Di, 10.09.2024, 09:15 - 15:00 Uhr, 4.B47
Mi, 11.09.2024, 09:15 - 15:00 Uhr, 4.B47
Do, 12.09.2024, 09:15 - 15:00 Uhr, 4.B47 |
| 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 |
To attend the course / exercise, registration via e-learning platform OLAT is required. Registration is possible from 26 August – 9 September 2024. The students themselves are responsible for checking the creditability of the course to their course of study. |
| Prüfung |
Daily examinations during the course of the block course.
***IMPORTANT*** In order to acquire credits, resp. to take the examination, registration via the Uni Portal within 9 - 10 September 2024 is REQUIRED. Further information on registration: www.unilu.ch/wf/pruefungen |
| 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 |