- Lectures Mondays from 16:15 – 19:00 in HG G5
- Lecturers Nino Antulov-Fantulin and Patrick Cheridito
- Assistants 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 time series. It 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).
The following topics will be covered: complex systems, empirical facts in finance,
PyTorch, ensemble learning, neural networks, clustering, graph cuts,
matrix factorisation, reinforcement learning, MCMC, LSTM, attention mechanism, neural ODEs,
PINNs, transformers, Black–Litterman model
- Paszke, Adam, et al. "Pytorch: An imperative style, high-performance deep learning library." Advances in neural information processing systems 32 (2019).
- Ian Goodfellow, Yoshua Bengio and Aaron Courville (2020). Deep Learning. MIT Press.
- Mehta, Pankaj, et al. "A high-bias, low-variance introduction to machine
learning for physicists." Physics reports 810 (2019): 1-124.
- Tsay, Ruey S. Analysis of financial time series. John wiley & sons, 2005.
- Richmond, Peter, Jürgen Mimkes, and Stefan Hutzler. Econophysics and
physical economics. Oxford University Press, USA, 2013.