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401-3916-25L 5 Credits DR , MSC D-ITET , D-INFK , D-MATH
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Machine Learning for Finance and Complex Systems

Lecturers & Examiners: Prof. Dr. Patrick Cheridito, Dr. Nino Antulov-Fantulin
Maximal number of participants: 42
VVZ CR n/a

Last Updated: 2026-06-01 11:33:15

Abstract

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

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 time series; (ii) practical - to allow students to gain practical experience by working on a coding project based on a theoretical topic of part (i).

Content

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

Resources

Literature

[1] Paszke, Adam, et al. "Pytorch: An imperative style, high-performance deep learning library." Advances in neural information processing systems 32 (2019). [2] Ian Goodfellow, Yoshua Bengio and Aaron Courville (2020). Deep Learning. MIT Press. [3] Mehta, Pankaj, et al. "A high-bias, low-variance introduction to machine learning for physicists." Physics reports 810 (2019): 1-124. [4] Tsay, Ruey S. Analysis of financial time series. John wiley & sons, 2005. [5] Richmond, Peter, Jürgen Mimkes, and Stefan Hutzler. Econophysics and physical economics. Oxford University Press, USA, 2013.

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
12.02.2025

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