401-3915-DRL Machine Learning in Finance and Insurance
| Semester | Autumn Semester 2023 |
| Lecturers | P. Cheridito |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to info@zgsm.ch with their name, course number and student ID. Please see https://zgsm.math.uzh.ch/index.php?id=forum0 |
| Abstract | This course introduces machine learning methods that can be used in finance and insurance applications. | |||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal is to learn methods from machine learning that can be used in financial and insurance applications. | |||||||||||||||||||||||||||||||||||||||
| Content | Linear, polynomial, logistic, ridge and lasso regression, dimension reduction methods, singular value decomposition, kernel methods, support vector machines, classification and regression trees, random forests, XGBoost, neural networks, stochastic gradient descent, autoencoders, graph neural networks, transfomers, credit analytics, pricing, hedging, insurance claim prediction. | |||||||||||||||||||||||||||||||||||||||
| Lecture notes | More information on https://people.math.ethz.ch/~patrickc/mlfi | |||||||||||||||||||||||||||||||||||||||
| Literature | Matthew F. Dixon, Igor Halperin, Paul Bilokon (2020). Machine Learning in Finance. Springer. Ian Goodfellow, Yoshua Bengio and Aaron Courville (2020). Deep Learning. MIT Press. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2021). An Introduction to Statistical Learning. Springer. Marcos Lopez de Prado (2018). Advances in Financial Machine Learning. Wiley. Marcos Lopez de Prado (2020). Machine Learning for Asset Managers. Cambridge Elements. Mario V. Wüthrich and Michael Merz (2023). Statistical Foundations of Actuarial Learning and its Applications. Springer. | |||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course requires basic knowledge in analysis, linear algebra, probability theory and statistics. | |||||||||||||||||||||||||||||||||||||||
Competencies |
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