VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
401-3932-19L
6
Credits
MSC
D-MATH
You're viewing possible stale or outdated data. Please check the latest semester for more up-to-date information.
Machine Learning in Finance
Lecturers & Examiners:
Prof. Dr. Josef Teichmann
Last Updated: 2026-02-05 15:41:16
Abstract
The course will deal with the following topics with rigorous proofs and many coding excursions: Universal approximation theorems, Stochastic gradient Descent, Deepnetworks and wavelet analysis, Deep Hedging, Deep calibration,Different network architectures, Reservoir Computing, Time series analysis by machine learning, Reinforcement learning, generative adversersial networks, Economic games.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 90 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Machine Learning in Finance |
|
3 h weekly |
| exercise | Machine Learning in Finance |
|
1 h weekly |
Offered In
-
Quantitative Finance Master (see Students in the Joint Degree Master's Programme "Quantitative Finance" must book UZH modules directly at the UZH. Those modules are not listed here.)
-