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401-3931-00L 4 Credits BSC , DR , MSC D-MATH
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Responsible Machine Learning with Insurance Applications

Lecturers & Examiners: Michael Mayer, Christian Lorentzen-Geiser
VVZ CR n/a

Last Updated: 2026-02-05 16:14:49

Abstract

This lecture covers important aspects of applying supervised machine learning models in a responsible way, based on sound statistical theory. The focus is on model interpretability, calibration (bias) assessment, and proper model comparison. The methods are illustrated with actuarial datasets.

Objective

The student is familiar with the main tools of model interpretability, calibration assessment, and model comparison and knows how to apply supervised machine learning in a responsible way.

Content

• Overview of supervised machine learning (statistical learning theory, GLMs, tree based methods, and neural nets; cross-validation) • Model interpretability methods (partial dependence plots, measures of variable importance, and SHAP) • Bias/calibration assessment with identification functions • Model comparison with consistent scoring functions • Working with dependent observations and further topics

Resources

Learning Materials (Links)

General Information

Language
English
Levels
BSC , DR , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
oral 30 minutes

Course Components

Type Title Time & Place Hours
lecture with exercise Responsible Machine Learning with Insurance Applications
  • Mon 14:15-16:00 (CAB G 56)
2 h weekly

Offered In

  • Mathematics (General Courses)
  • Mathematics Bachelor
    • Electives
      • Selection: Financial and Insurance Mathematics (In the Bachelor's programme in Mathematics 401-3913-01L Mathematical Foundations for Finance is eligible as an elective course, but only if 401-3888-00L Introduction to Mathematical Finance isn't recognised for credits (neither in the Bachelor's nor in the Master's programme). For the category assignment take contact with the Study Administration Office ( ) after having received the credits.)
  • Mathematics Master
    • Electives (For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 14 of the required 26 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.)
  • Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
  • Quantitative Finance Master (see Students in the Joint Degree Master's Programme "Quantitative Finance" must book University of Zurich modules directly at the University of Zurich. Those modules are not listed here.)