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401-3932-DRL 2 Credits DR D-MATH

Mathematics for New Technologies in Finance

Lecturers & Examiners: Prof. Dr. Josef Teichmann, Prof. Dr. Philipp Harms
Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to with their name, course number and student ID. Please see Formerly until FS22: Machine Learning in Finance
VVZ CR n/a

Last Updated: 2026-02-05 16:37:26

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.

Resources

Learning Materials (Links)

General Information

Language
English
Levels
DR
Frequency
Yearly recurring

Examination

Type
ungraded semester performance

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture Mathematics for New Technologies in Finance
  • Mon 10:15-12:00 (HG G 5)
  • Wed 11:15-12:00 (HG G 3)
3 h weekly
exercise Mathematics for New Technologies in Finance
  • Wed 10:15-11:00 (HG E 21)
  • Wed 10:15-11:00 (LEE D 101)
1 h weekly

Offered In