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401-3915-73L 5 Credits DR , MSC D-ITET , D-MATH , D-INFK
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Machine Learning in Finance and Insurance

Lecturers & Examiners: Prof. Dr. Patrick Cheridito
VVZ CR n/a

Last Updated: 2026-02-05 16:30:05

Abstract

This course introduces machine learning methods that can be used in finance and insurance applications.

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.

Resources

Lecture Notes

Course material is available onhttps://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.

Learning Materials (Links)

General Information

Language
English
Levels
DR , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
10 single-sided A4 pages of notes. No books or lecture notes. Laptops, tablets and mobile phones must be switched off.
Practical projects are an integral part of the course. Participation is mandatory.The grade for the course will be calculated as a weighted average of the grade achieved in the final exam (70%) and the grade achieved in the practical projects (30%).

Course Components

Type Title Time & Place Hours
lecture Machine Learning in Finance and Insurance
  • Tue 16:15-18:00 (ML D 28)
2 h weekly
exercise Machine Learning in Finance and Insurance
  • Wed 16:15-17:00 (HG F 5)
1 h weekly

Offered In