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401-3932-19L 4 Credits DR , MSC D-MATH
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Mathematics for New Technologies in Finance

Lecturers & Examiners: Prof. Dr. Josef Teichmann, Prof. Dr. Philipp Harms
formerly until FS22: Machine Learning in Finance
VVZ CR n/a

Last Updated: 2026-06-01 11:33:19

Abstract

Rigorous proofs & many coding excursions for the following topics: 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 adversarial networks, Economic games, Large Language Models in Finance.

Content

This 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 adversarial networks, Economic games, Large Language Models in Finance.

Resources

Learning Materials (Links)

General Information

Language
English
Levels
DR , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 90 minutes
Aids
None

Course Components

Type Title Time & Place Hours
lecture Mathematics for New Technologies in Finance
  • Wed 10:15-13:00 (HG G 3)
3 h weekly
exercise Mathematics for New Technologies in Finance
  • Mon 13:15-14:00 (HG D 3.2)
  • Mon 13:15-14:00 (HG D 7.2)
1 h weekly

Offered In