Machine Learning Methods in Non-Life Insurance

Course content

  • 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.

 

Education

MSc Programme in Actuarial Mathematics

MSc Programme in Mathematics-Economics

MSc Programme in Statistics

Learning outcome

Knowledge:

  • 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


Skills:

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


Competences:

  • Know how to use R to solve practical problems

4 hours of lectures per week for 7 weeks

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.

Collective
ECTS
7,5 ECTS
Type of assessment
Oral examination under invigilation
Type of assessment details
30min oral examination (without preparation time).
Aid
Without aids
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assessment

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

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 124
  • Project work
  • 42
  • Exam
  • 12
  • English
  • 206

Kursusinformation

Language
English
Course number
NMAK22019U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 3
Schedulegroup
B
Capacity
No limit.
The number of seats may be reduced in the late registration period
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Munir Hiabu   (2-7c774f7c7083773d7a843d737a)
Saved on the 07-04-2022

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