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401-3931-00L 4 Credits BSC , DR , MSC D-MATH

Responsible Machine Learning with Insurance Applications

VVZ CR n/a

Last Updated: 2026-06-03 00:07:54

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

General Information

Language
English
Levels
BSC , DR , MSC
Frequency
Every two years

Examination

Type
session examination
Mode
oral 30 minutes

Course Components

Type Title Time & Place Hours
lecture with exercise Responsible Machine Learning with Insurance Applications No time listed 2 h weekly

Offered In

  • "PBLabs" Pilot
  • Computational Science and Engineering Bachelor
    • Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
  • Mathematics Bachelor
    • Core Courses and Elective Courses
      • 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.)
  • Computational Science and Engineering Master
    • Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed. All courses listed as core courses (not electives) for one of the following ETH MSc programmes, MSc Statistics, MSc Physics, MSc Computer Science, MSc (Applied) Mathematics, MSc Neural Systems and Computation, MSc Robotics, Systems, and Control, MSc Data Science, MSc Electrical Engineering and Information Technology, can be taken as an elective course in the MSc CSE without prior permission.)
  • 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.)