Universität Wien

040222 SE Seminar in Statistics for Master Studies (2024S)

3.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
MIXED
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: English

Lecturers

Classes (iCal) - next class is marked with N

  • Friday 01.03. 13:15 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Friday 08.03. 13:15 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 13.03. 11:30 - 14:45 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
  • Friday 15.03. 13:15 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 20.03. 11:30 - 14:45 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
  • Friday 22.03. 13:15 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Friday 12.04. 11:30 - 14:45 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
  • Monday 27.05. 11:30 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Monday 03.06. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock

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

Slides, Article, Ipython Notebooks

Association in the course directory

Last modified: Th 28.03.2024 08:45