| Dozent/in |
Dr. Markus Meierer; Dr. oec. Margot Löwenberg |
| Veranstaltungsart |
Vorlesung |
| Code |
HS221234 |
| Semester |
Herbstsemester 2022 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Bachelor |
| Termin/e |
Mo, 12.09.2022, 09:00 - 14:00 Uhr, 4.B54
Di, 13.09.2022, 09:15 - 14:00 Uhr, 4.B51
Mi, 14.09.2022, 09:15 - 14:00 Uhr, 4.B51
Do, 15.09.2022, 09:15 - 14:00 Uhr, 4.B51
Fr, 16.09.2022, 09:15 - 14:00 Uhr, HS 14 |
| 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 lecture, registration via e-learning platform OLAT is required. Registration is possible from 29 August - 16 September 2022. The students themselves are responsible for checking the creditability of the course to their course of study.
Direct link to OLAT course:
https://lms.uzh.ch/url/RepositoryEntry/17250386181
The UniPortal registration at the beginning of the semester is mandatory for this lecture so that the credits can be credited. It is no longer possible to cancel your registration after 12 September 2022. |
| Prüfung |
Daily examinations during the course of the block course.
***IMPORTANT*** In order to acquire credits, resp. to take the examinationIn, registration via the Uni Portal within 29.08. - 12.09.2021 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@business.uzh.ch |