401-3915-73L Machine Learning in Finance and Insurance
| Semester | Herbstsemester 2023 |
| Dozierende | P. Cheridito |
| Periodizität | jährlich wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
| Kurzbeschreibung | This course introduces machine learning methods that can be used in finance and insurance applications. | |||||||||||||||||||||||||||||||||||||||
| Lernziel | The goal is to learn methods from machine learning that can be used in financial and insurance applications. | |||||||||||||||||||||||||||||||||||||||
| Inhalt | 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. | |||||||||||||||||||||||||||||||||||||||
| Skript | Course material is available on https://people.math.ethz.ch/~patrickc/mlfi | |||||||||||||||||||||||||||||||||||||||
| Literatur | 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. | |||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The course requires basic knowledge in analysis, linear algebra, probability theory and statistics. | |||||||||||||||||||||||||||||||||||||||
Kompetenzen |
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