401-3932-19L Machine Learning in Finance
| Semester | Frühjahrssemester 2021 |
| Dozierende | J. Teichmann |
| Periodizität | jährlich wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
| Kurzbeschreibung | The course will deal with the following topics with rigorous proofs and many coding excursions: Universal approximation theorems, Stochastic gradient Descent, Deep networks 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. |
| Lernziel | |
| Voraussetzungen / Besonderes | Bachelor in mathematics, physics, economics or computer science. |