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401-3932-19L 4 Credits 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: 2026-02-05 16:37:23

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.

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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	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

Computational Science and Engineering Master
- Fields of Specialization
  - Computational Finance
Mathematics Master
- Electives (For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 14 of the required 26 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.)
  - Electives: Applied Mathematics and Further Application-Oriented Fields (¬)
    - Selection: Financial and Insurance Mathematics
Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
- Subject Specific Electives
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.)
- Core
  - MF (Mathematical Methods in Finance) (For possible additional course offerings see )