| Dozent/in |
Dr. Markus Meierer; |
| Veranstaltungsart |
Vorlesung |
| Code |
HS201009 |
| Semester |
Herbstsemester 2020 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Bachelor |
| Termin/e |
Fr, 16.10.2020, 09:15 - 14:00 Uhr, Online
Sa, 17.10.2020, 09:15 - 14:00 Uhr, Online
Fr, 23.10.2020, 09:15 - 14:00 Uhr, Online
Sa, 24.10.2020, 09:15 - 14:00 Uhr, Online
Fr, 30.10.2020, 09:15 - 14:00 Uhr, Online |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
blocked
Zoom Live Stream |
| 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, decision trees, random forest, support vector, machines, deep learning, and ensemble methods are among the topics to be discussed in this course. This class is a lecture with integrated exercises. For every session, you are required to bring your laptop (with the latest version of your operating system installed). We do only require basic knowledge of R (e.g., how to load a CSV file and install a package). Further, we do require the willingness to actively participate and contribute to the class. |
| Lernziele |
(1) Get familiar with the concept of (supervised) machine learning.
(2) Understand the basic theory behind various machine learning techniques.
(3) Apply different machine learning techniques and interpret the results. |
| Voraussetzungen |
(1) Bring a laptop (with the latest operating system version installed).
(2) Updated installation of R (https://cran.r-project.org/).
(3) Updated installation of Rstudio (https://www.rstudio.com/).
(4) Basic knowledge in R (e.g., how to load a CSV file and how to install a package). |
| Sprache |
Englisch |
| Anmeldung |
To attend the lecture, registration via e-learning platform OLAT is required. Registration is possible from August, 31 to September 25, 2020. 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/16800973074 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 October 16, 2020. |
| Leistungsnachweis |
***IMPORTANT*** In order to take part in the examination, registration via the Uni Portal within 01.10. - 16.10.2020 is REQUIRED. |
| Abschlussform / Credits |
Daily exams, final exam, online exercises / 3 Credits
|
| Hinweise |
- Daily exams (1-2 questions; focus on programming skills)
- Final exam (20 multiple-choice questions; focus on knowledge questions)
- Online exercises (The online exercises have to be finished within three weeks after the last lecture.)
|
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
markus.meierer@uzh.ch |