| Dozent/in |
Dr. rer. publ. Massimo Mannino |
| Veranstaltungsart |
Vorlesung |
| Code |
HS241111 |
| Semester |
Herbstsemester 2024 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Fr, 27.09.2024, 08:15 - 14:00 Uhr, 4.B51
Fr, 11.10.2024, 08:15 - 14:00 Uhr, HS 14
Fr, 25.10.2024, 08:15 - 14:00 Uhr, 3.B57
Fr, 06.12.2024, 08:15 - 14:00 Uhr, 3.B57 |
| Umfang |
Blockveranstaltung |
| Turnus |
Block course |
| Inhalt |
The lecture familiarizes students with a wide range of models in the field of Supervised Machine Learning. The course will focus on practical machine learning applications and teach data science techniques that enable students to solve real-world problems from the business world. By means of R, students will learn to estimate and visualize model results and communicate results efficiently. The integrated exercises discuss application examples from business administration and economics. |
| Lernziele |
1) Students can independently prepare and analyze data with R.
2) Students can apply methods in the field of Supervised Machine Learning.
3) Students are able to visualize model results with R.
4) Students can communicate model results effectively. |
| Voraussetzungen |
Solid knowledge in econometrics, statistics and R. |
| Sprache |
Englisch |
| Begrenzung |
24 Students |
| Anmeldung |
To attend the course / exercise, registration via e-learning platform OLAT is required. Registration is possible from 2 – 27 September 2024. The students themselves are responsible for checking the creditability of the course to their course of study. |
| Prüfung |
***IMPORTANT*** In order to acquire credits, resp. to take the examination, registration via the Uni Portal within 16 - 27 September 2024 is ESSENTIALLY REQUIRED. Further information on registration: www.unilu.ch/wf/pruefungen |
| Abschlussform / Credits |
Written paper / Individual or group presentation / 3 Credits
|
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
massimo.mannino@novalytica.com |
| Anzahl Anmeldungen |
13 von maximal 24 |
| Literatur |
An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani).
Freely available at: http://faculty.marshall.usc.edu/gareth-james/ |