Machine Learning in Finance & Insurance

Fall 2023, ETH Zurich



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.


  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