040222 SE Seminar in Statistics for Master Studies (2023S)

3.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften

Continuous assessment of course work

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 is open from Mo 13.02.2023 09:00 to We 22.02.2023 12:00
Registration is open from Mo 27.02.2023 09:00 to Tu 28.02.2023 12:00
Deregistration possible until Fr 17.03.2023 23:59

Details

max. 24 participants

Language: English

Lecturers

Christa Cuchiero

Classes (iCal) - next class is marked with N

Monday 06.03. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
Monday 20.03. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
Monday 27.03. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
Monday 17.04. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
Monday 24.04. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
Thursday 04.05. 13:15 - 14:45 Seminarraum 15, Kolingasse 14-16, OG01
Monday 15.05. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
Friday 26.05. 09:45 - 11:15 Digital
Seminarraum 10, Kolingasse 14-16, OG01
Thursday 01.06. 13:15 - 14:45 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Monday 12.06. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01
Monday 19.06. 11:30 - 13:00 Seminarraum 10, Kolingasse 14-16, OG01

Information

Aims, contents and method of the course

Deep learning and applications in Finance

The 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%, also in distance learning mode)
presentation (60%)
programming part (30%)

Examination topics

presented content

Reading list

Folien, Artikel, Ipython Notebooks

Association in the course directory

Last modified: Th 01.06.2023 16:07