| Dozent/in |
Dr. rer. pol. Markus Johannes Meierer; Patrick Bachmann, MA |
| Veranstaltungsart |
Vorlesung |
| Code |
HS191581 |
| Semester |
Herbstsemester 2019 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Bachelor
Master |
| Termin/e |
Fr, 18.10.2019, 08:15 - 12:00 Uhr, HS 10
Sa, 19.10.2019, 08:15 - 12:00 Uhr, HS 5
Fr, 25.10.2019, 08:15 - 12:00 Uhr, HS 10
Sa, 26.10.2019, 08:15 - 12:00 Uhr, HS 5 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
blocked |
| Inhalt |
Machine learning has become one of the core pillars of information technology. Since the amount of available data is steadily increasing, smart data analysis 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, deci-sion trees, random forest, support vector, machines, deep learning, and ensemble methods are among the topics to be discussed in this course.
|
| 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, enrolment via the e-learning platform OLAT is required. Registration is possible from 2 to 27 September 2019. The students themselves are responsible for checking the creditability of the course to their degree programme. Direct link to the OLAT course: https://lms.uzh.ch/url/repositoryentry/16616980739 |
| Leistungsnachweis |
Multiple-choice tests, online exercises, group work |
| Abschlussform / Credits |
Multiple-choice tests, online exercises, group work / 3 Credits
|
| Hörer-/innen |
Ja |
| Kontakt |
markus.meierer@uzh.ch |