240165 High-Dimensional Statistics (VÜA) (SoSe 2023)
Contents, comment
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The goal of this course is to provide a rigorous introduction to concepts and methods of high-dimensional statistics having numerous applications in data science, machine learning and signal processing.
Topics (tentative):
Compressed sensing and sparse recovery
Matrix completion and the Netflix problem
Principal component analysis and spectral clustering in high dimensions
Kernel methods and support vector machines
Overparametrization and the double descent phenomenon
Requirements for participation, required level
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Stochastik, Lineare Algebra, Analysis
Bibliography
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High-Dimensional Statistics by Martin J. Wainwright
High-Dimensional Probability by Roman Vershynin
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period |
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Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 24-M-P1 Profilierung 1 | Profilierungsvorlesung (mit Übung) - Typ 3 | Study requirement
|
Student information |
| 24-M-P1a Profilierung 1 Teil A | Profilierungsvorlesung (mit Übung) - Typ 3 | Study requirement
|
Student information |
| 24-M-P1b Profilierung 1 Teil B | Profilierungsvorlesung (mit Übung) - Typ 3 | Student information | |
| 24-M-P2 Profilierung 2 | Profilierungsvorlesung (mit Übungen) - Typ 2 | Study requirement
|
Student information |
| 24-M-PWM Profilierung Wirtschaftsmathematik | Profilierungsvorlesung (mit Übung) -Typ 3 | Study requirement
|
Student information |
| 31-M-ASM2 Advanced Statistical Methods II | Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (I.) | Graded examination
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Student information |
| Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (II.) | Graded examination
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Student information | |
| 31-SW-AKStat Ausgewählte Kapitel der Statistik | Veranstaltung aus dem Bereich Statistik oder einem methodisch verbundenen Gebiet 4 LP | Graded examination
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Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
E-Learning Space
A corresponding course offer for this course already exists in the e-learning system. Teaching staff can store materials relating to teaching courses there:
- Registered number: 15
- This is the number of students having stored the course in their timetable. In brackets, you see the number of users registered via guest accounts.
- Address:
- SS2023_240165@ekvv.uni-bielefeld.de
- This address can be used by teaching staff, their secretary's offices as well as the individuals in charge of course data maintenance to send emails to the course participants. IMPORTANT: All sent emails must be activated. Wait for the activation email and follow the instructions given there.
- If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_398334405@ekvv.uni-bielefeld.de
- Coverage:
- 14 Students to be reached directly via email
- Notes:
- Additional notes on the electronic mailing lists
- Email archive
- Number of entries 1
- Open email archive
- Last update basic details/teaching staff:
- Wednesday, January 11, 2023
- Last update times:
- Wednesday, April 12, 2023
- Last update rooms:
- Wednesday, April 12, 2023
- Type(s) / SWS (hours per week per semester)
- VÜA / 3
- Language
- This lecture is taught in english
- Department
- Faculty of Mathematics
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- https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=398334405
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- ID
- 398334405