040222 SE Seminar in Statistics for Master Studies (2025S)
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 10.02.2025 09:00 to Tu 18.02.2025 12:00
- Registration is open from We 26.02.2025 09:00 to Th 27.02.2025 12:00
- Deregistration possible until Fr 14.03.2025 23:59
Details
max. 24 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 04.03. 11:30 - 13:00 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 18.03. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- Tuesday 25.03. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- Tuesday 01.04. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- Tuesday 08.04. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- Tuesday 29.04. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- Thursday 15.05. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
- Tuesday 20.05. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- Tuesday 27.05. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- Tuesday 03.06. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- Tuesday 10.06. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- Tuesday 17.06. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
- N Tuesday 24.06. 11:30 - 13:00 Seminarraum 10, 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
Slides, Articles, Ipython Notebooks
Association in the course directory
Last modified: Th 17.04.2025 13:45