- Lectures Mondays from 16:15 – 19:00 in HG G5
- Lecturers Nino Antulov-Fantulin and Patrick Cheridito
- Assistants Aleksandar Arandjelovic, Jean-Loup Dupret, Urban Ulrych and Gabriele Visentin
- Prerequisites
Machine Learning in Finance & Insurance
- More information on Moodle
This course introduces machine learning methods and frameworks that can be used for modelling and analysing complex systems with a particular focus on financial applications. It 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).
The following topics will be covered: complex systems, empirical facts in finance,
PyTorch, ensemble learning, neural networks, clustering, Graph Cut,
matrix factorisation, reinforcement learning, MCMC, LSTM, attention mechanism, neural ODEs,
PINNs, transformers, Black–Litterman model
- Ian Goodfellow, Yoshua Bengio and Aaron Courville (2020). Deep Learning. MIT Press.
- Pankaj Mehta et al. (2019). A high-bias, low-variance introduction to machine
learning for physicists. Physics Reports 810 (2019): 1-124.
- Stefan Nagel (2021). Machine Learning in Asset Pricing. Princeton University Press.
- Giuseppe A. Paleologo (2025). The Elements of Quantitative Investing. John Wiley & Sons, 2025..
- Adam Paszke et al. (2019). Pytorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32.
- Peter Richmond, Jürgen Mimkes and Stefan Hutzler (2013). Econophysics and
Physical Economics. Oxford University Press, USA.
- Ruey S. Tsay (2005). Analysis of Financial Time Series. John Wiley & Sons.