NMAK22019U Machine Learning Methods in Non-Life Insurance

Volume 2022/2023

MSc Programme in Actuarial Mathematics

MSc Programme in Mathematics-Economics

MSc Programme in Statistics

  • Introduction of various machine learning methods. Topics may include but are not limited to: theory of penalized linear regression, splines, additive models, neural networks, multivariate adaptive splines, projection pursuit regression, regression trees, random forests, boosting. 
  • Discussoion on interpretability. Various topics on interpretable machine learning, including global model-agnostic methods like Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) plots as well as local model-agnostic methods like Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP) values.


Learning Outcome


  • Regression with classical (penalized) methods as well as machine learning methods
  • Classification with classical methods as well as machine learning methods
  • Various machine learning interpretation methods


A general ability to use machine learning methods to solve practical problems


  • Know how to use R to solve practical problems

Lecture notes 

Non-life insurance 2 (Skade 2) or similar. A class in regression is very useful. It is possible to follow the class without these, but of course it will be more demanding.

Academic qualifications equivalent to a BSc degree is recommended.
4 hours of lectures per week for 7 weeks
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 124
  • Project work
  • 42
  • Exam
  • 12
  • Total
  • 206
7,5 ECTS
Type of assessment
Oral examination under invigilation
Type of assessment details
30min oral examination (without preparation time).
Exam registration requirements

Two mandatory assignments must be approved and valid before the student is allowed attending the exam.

Without aids
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Same as ordinary exam. If the the two mandatory homework assignments were not approved before the ordinary exam they must be resubmitted. They must be approved three weeks before the beginning of the re-exam week.

Criteria for exam assesment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.