Universität Wien FIND

Return to Vienna for the summer semester of 2022. We are planning to hold courses mainly on site to enable the personal exchange between you, your teachers and fellow students. We have labelled digital and mixed courses in u:find accordingly.

Due to COVID-19, there might be changes at short notice (e.g. individual classes in a digital format). Obtain information about the current status on u:find and check your e-mails regularly.

Please read the information on https://studieren.univie.ac.at/en/info.

040170 UK Statistics of high-dimensional and complex data (2020W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

Summary

1 Milovic , Moodle
2 Milovic , Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Registration information is available for each group.

Groups

Group 1

Die Literatur zu dem Thema dieses Kurses ist durchweg auf Englisch. Daher sind auch die Kursmaterialien auf Englisch. Der Kurs kann auf Wunsch auf Deutsch gehalten werden, wobei es sinnvoller wäre den Kurs komplett auch auf Englisch zu halten.

max. 35 participants
Language: German
LMS: Moodle

Lecturers

Classes (iCal) - next class is marked with N

This course will be held online. All materials and information will be continuously updated in Moodle, which means that it is not necessary to be online on Mondays at 16:45.

Monday 05.10. 16:45 - 18:15 Digital
Monday 12.10. 16:45 - 18:15 Digital
Monday 19.10. 16:45 - 18:15 Digital
Monday 09.11. 16:45 - 18:15 Digital
Monday 16.11. 16:45 - 18:15 Digital
Monday 23.11. 16:45 - 18:15 Digital
Monday 07.12. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
Monday 14.12. 16:45 - 18:15 Digital
Monday 11.01. 16:45 - 18:15 Digital
Monday 18.01. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß

Group 2

max. 35 participants
Language: German
LMS: Moodle

Lecturers

Classes (iCal) - next class is marked with N

This course will be held online. All materials and information will be continuously updated in Moodle, which means that it is not necessary to be online on Mondays at 16:45.

Monday 05.10. 15:00 - 16:30 Digital
Monday 12.10. 15:00 - 16:30 Digital
Monday 19.10. 15:00 - 16:30 Digital
Monday 09.11. 15:00 - 16:30 Digital
Monday 16.11. 15:00 - 16:30 Digital
Monday 23.11. 15:00 - 16:30 Digital
Monday 30.11. 15:00 - 16:30 Digital
Monday 07.12. 15:00 - 16:30 Digital
Monday 14.12. 15:00 - 16:30 Digital
Monday 11.01. 15:00 - 16:30 Digital
Monday 18.01. 15:00 - 16:30 Digital
Monday 25.01. 15:00 - 16:30 Digital

Information

Aims, contents and method of the course

High-dimensional linear models, model selection, LASSO, Ridge, Multiple Testing, etc.

Assessment and permitted materials

2 exams + homework

Minimum requirements and assessment criteria

Examination topics

Reading list

Hastie, T.; Tibshirani, R. & Friedman, J. (2001), The Elements of Statistical Learning , Springer New York Inc. , New York, NY, USA .

https://web.stanford.edu/~hastie/ElemStatLearn/

Association in the course directory

Last modified: Mo 05.10.2020 10:08