040222 SE Seminar in Statistics for Master Studies (2021S)

3.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften

Continuous assessment of course work

REMOTE

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 Th 11.02.2021 09:00 to Mo 22.02.2021 12:00
Registration is open from Th 25.02.2021 09:00 to Fr 26.02.2021 12:00
Deregistration possible until We 31.03.2021 23:59

Details

max. 24 participants

Language: German

Lecturers

Christa Cuchiero

Classes (iCal) - next class is marked with N

Since face-to-face teaching is currently not possible, the seminar will begin digitally and synchronously at the above-mentioned dates. If the situation changes during the semester, a hybrid variant can be considered.

Wednesday 03.03. 15:00 - 16:30 Digital
Wednesday 10.03. 13:15 - 14:45 Digital
Tuesday 16.03. 13:15 - 14:45 Digital
Wednesday 24.03. 15:00 - 16:30 Digital
Wednesday 14.04. 15:00 - 16:30 Digital
Wednesday 21.04. 11:30 - 13:00 Digital
Wednesday 28.04. 15:00 - 16:30 Digital
Wednesday 05.05. 15:00 - 16:30 Digital
Wednesday 12.05. 15:00 - 16:30 Digital
Wednesday 19.05. 13:15 - 14:45 Digital
Wednesday 26.05. 15:00 - 16:30 Digital
Wednesday 02.06. 15:00 - 16:30 Digital
Wednesday 09.06. 15:00 - 16:30 Digital
Wednesday 16.06. 15:00 - 16:30 Digital
Wednesday 23.06. 15:00 - 16:30 Digital
Wednesday 30.06. 15:00 - 16:30 Digital

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 and deep calibration.
These topics and small 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

Slides, Ipython Notebooks

Association in the course directory

In-Depth Studies Statistics (39 ECTS)

Magister Statistics (951) ➡ Core Modules (90 ECTS)

Last modified: Fr 12.05.2023 00:12