Universität Wien
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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).

Details

max. 24 participants
Language: German

Lecturers

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

Last modified: Fr 12.05.2023 00:12