401-3916-25L Machine Learning for Finance and Complex Systems
| Semester | Spring Semester 2025 |
| Lecturers | N. Antulov-Fantulin, P. Cheridito |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Maximal number of participants: 42 |
| 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. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Learning 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. | ||||||||||||||||||||||||||||||||||||||||||||||||
| 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. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Machine Learning in Finance and Insurance. Max 40 students, due to guided projects. Topics are defined at the beginning of the course. They consist of different research papers that have to be analyzed, reproduced and potentially extended. | ||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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