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401-3916-25L 5 Credits DR , MSC D-ITET , D-INFK , D-MATH

Machine Learning for Finance and Complex Systems

Lecturers & Examiners: Prof. Dr. Patrick Cheridito, Dr. Nino Antulov-Fantulin
Maximal number of participants: ca. 40. Fully booked, in fact, already overbooked! [As of 4 February 09:45 there were 56 definitive registrations, 91 students waitlisted]
VVZ CR n/a

Last Updated: 2026-06-03 00:14:14

Abstract

This course introduces machine learning methods that can be used for modelling and analysing complex systems with a particular focus on financial applications.

Objective

The course has two main objectives: (i) theoretical - to provide an overview of machine learning methods with a focus on complex systems and financial applications; (ii) practical - to allow students to gain practical experience by working on a coding project based on a theoretical topic of part (i).

Content

Complex systems, empirical facts in finance, introduction to PyTorch, ensemble learning, neural networks, clustering, Graph Cut, matrix factorisation, reinforcement learning, MCMC, LSTM, attention mechanism, neural ODEs, PINNs, transformers, Black–Litterman model.

Resources

Literature

[1] Ian Goodfellow, Yoshua Bengio and Aaron Courville (2020). Deep Learning. MIT Press. [2] Pankaj Mehta et al. (2019). A high-bias, low-variance introduction to machine learning for physicists. Physics Reports 810 (2019): 1-124. [3] Stefan Nagel (2021). Machine Learning in Asset Pricing. Princeton University Press. [4] Giuseppe A. Paleologo (2025). The Elements of Quantitative Investing. John Wiley & Sons, 2025.. [5] Adam Paszke et al. (2019). Pytorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32. [6] Peter Richmond, Jürgen Mimkes and Stefan Hutzler (2013). Econophysics and Physical Economics. Oxford University Press, USA. [7] Ruey S. Tsay (2005). Analysis of Financial Time Series. John Wiley & Sons.

Learning Materials (Links)

General Information

Language
English
Levels
DR , MSC
Frequency
Yearly recurring

Examination

Type
graded semester performance
Grading: Projects (70%), Quizzes (30%)

Registration & Places

Limited places (Special selection)
Signup End
11.02.2026

Course Components

Type Title Time & Place Hours
lecture with exercise Machine Learning for Finance and Complex Systems
  • Mon 16:15-19:00 (HG G 5)
3 h weekly

Offered In