Info

• Lectures Tuesdays from 16:15 – 18:00 in ML D28
• Exercise Sessions Wednesdays from 17:15 – 18:00 in HG E1.1
• Lecturers Patrick Cheridito, Aleksey Minabutdinov, Arishi Orra and Zhexin Wu
• 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. Deep learning
8. Gen AI
9. Classification and regression trees
10. Bagging and random forests
11. Gradient boosted trees

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.