040170 UK Statistics of high-dimensional and complex data (2023W)
Continuous assessment of course work
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Summary
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 is open from Mo 11.09.2023 09:00 to Fr 22.09.2023 12:00
- Registration is open from Tu 26.09.2023 09:00 to We 27.09.2023 12:00
- Deregistration possible until Fr 20.10.2023 23:59
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
- Wednesday 04.10. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 11.10. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 18.10. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 25.10. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 08.11. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 15.11. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 22.11. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 29.11. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 06.12. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 13.12. 08:00 - 09:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 10.01. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 17.01. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 24.01. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 31.01. 08:00 - 09:30 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
Aims, contents and method of the course
Examination topics
Statistical theory presented in the lecture plus practical skills in R are necessary for this course.
Group 2
max. 35 participants
Language: German
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 04.10. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 11.10. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 18.10. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 25.10. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 08.11. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 15.11. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 22.11. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 29.11. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 06.12. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 13.12. 08:00 - 09:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 10.01. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 17.01. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 24.01. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 31.01. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
Aims, contents and method of the course
The aim of the course is to provide students with the necessary statistical toolkit to analyze
and extract information from data in various forms. The course combines the elements of
Statistics, Data Mining and Econometrics. All presented methods will be accompanied with
real-world examples and their implementations in R statistical software.Course contents: High-dimensional linear models, model selection, LASSO, Ridge, Multiple Testing, etc.
and extract information from data in various forms. The course combines the elements of
Statistics, Data Mining and Econometrics. All presented methods will be accompanied with
real-world examples and their implementations in R statistical software.Course contents: High-dimensional linear models, model selection, LASSO, Ridge, Multiple Testing, etc.
Examination topics
Die in der Vorlesung vorgestellte statistische Theorie sowie praktische Kenntnisse in R sind für diesen Kurs erforderlich.
Information
Assessment and permitted materials
Midterm exam (on-site)+ Project in R at the end of the semester
Minimum requirements and assessment criteria
Two partial assessments 2*50% of the final grade. To complete the course positively, you must achieve at least 60% of the points.
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/R Refresher:
R Graphics Cookbook: Practical Recipes for Visualizing Data - Winston ChangAlle anderen relevanten Informationen werden in Moodle veröffentlicht.
https://web.stanford.edu/~hastie/ElemStatLearn/R Refresher:
R Graphics Cookbook: Practical Recipes for Visualizing Data - Winston ChangAlle anderen relevanten Informationen werden in Moodle veröffentlicht.
Association in the course directory
Last modified: We 22.11.2023 13:07
and extract information from data in various forms. The course combines the elements of
Statistics, Data Mining, and Econometrics. All presented methods will be accompanied with
real-world examples and their implementations in R statistical software.Course contents: High-dimensional linear models, model selection, LASSO, Ridge, Multiple Testing, etc.