Info

• 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

Content

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.

Syllabus

1. Basic notions of statistical learning
2. Linear regression
3. (Stochastic) gradient descent
4. Logistic regression
5. Kernel methods
6. Neural networks
7. Classification and regression trees
8. Bagging and random forests
9. Gradient boosted trees
10. Graph neural networks and tranformers
11. Dimensionality reduction and autoencoders

Coding projects

1. Pricing with linear regression
2. Credit analytics
3. Deep hedging
4. Insurance claim prediction

Books

• 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.