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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.