401-3932-DRL  Mathematics for New Technologies in Finance

SemesterFrühjahrssemester 2023
DozierendeJ. Teichmann
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch
KommentarOnly for ETH D-MATH doctoral students and for doctoral students from the Institute of Mathematics at UZH. The latter need to send an email to Jessica Bolsinger (info@zgsm.ch) with the course number. The email should have the subject „Graduate course registration (ETH)“.

Formerly until FS22: Machine Learning in Finance


KurzbeschreibungThe 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