040222 SE Seminar in Statistics for Master Studies (MA) (2026S)
Continuous assessment of course work
Labels
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 09.02.2026 09:00 to Tu 17.02.2026 12:00
- Registration is open from Tu 24.02.2026 09:00 to We 25.02.2026 12:00
- Deregistration possible until Sa 14.03.2026 23:59
Details
max. 24 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 03.03. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 10.03. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 17.03. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 24.03. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 14.04. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 21.04. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 28.04. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 05.05. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 12.05. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- N Tuesday 19.05. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 26.05. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 02.06. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 09.06. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 16.06. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 23.06. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
- Tuesday 30.06. 11:30 - 13:00 Seminarraum 7, Kolingasse 14-16, OG01
Information
Aims, contents and method of the course
Deep learning and applications in FinanceThe course introduces several fundamental concepts from machine and deep learning, sheds light on statistical aspects and treats important applications in finance.It will cover topics like universal approximation theorems, stochastic gradient descent, neural ordinary differential equations and backpropagation. The financial applications include deep hedging, deep portfolio optimization, deep simulation, deep calibration, deep reinforcement learning and signature methods in finance.These topics and projects will be explored and presented by the lecturer and the students.
Assessment and permitted materials
Presentation of a paper or a chapter of the lecture notes and programming project
Minimum requirements and assessment criteria
active participation in class (10%)
presentation (60%)
programming part (30%)
presentation (60%)
programming part (30%)
Examination topics
presented content
Reading list
Folien, Artikel, Ipython Notebooks
Association in the course directory
Last modified: Mo 02.03.2026 13:06