**Lectures**Tuesdays from 16:15 – 18:00 in HG D7.1**Exercise Sessions**Wednesdays from 16:15 – 17:00 in HG D1.1**Lecturers**Patrick Cheridito, Stephan Eckstein and Gabriele Visentin**Prerequisites**Basic knowledge in analysis, linear algebra, probability theory & statistics**More information**on Moodle

This course introduces different machine learning methods and discusses their application to problems in finance and insurance. Topics include 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.

- Basic notions of statistical learning
- Linear regression
- (Stochastic) gradient descent
- Logistic regression
- Kernel methods
- Neural networks
- Classification and regression trees
- Bagging and random forests
- Gradient boosted trees
- Graph neural networks and tranformers
- Dimensionality reduction and autoencoders

- Pricing with linear regression
- Credit analytics
- Deep hedging
- Insurance claim prediction

- 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.