NMAK23006U Interpretable Machine Learning

Volume 2024/2025

MSc Programme in Actuarial Mathematics

MSc Programme in Mathematics-Economics

MSc Programme in Statistics


We will cover various topics on supervised learning (regression, classification) on tabular data:

  • Fundamentals of statistical learning
  • Linear models with and without penalization
  • Course of dimensionality in nonparametric models
  • Additive models
  • Tree based methods and neural networks
  • Post-hoc interpretability
Learning Outcome


  • Various regression & classification methods
  • Various post-hoc interpretation methods
  • Understand the inner working and limitations of those methods



  • A general ability to use and the select the right machine learning method to solve practical problems
  • Use R to to execute above point


  • Critically assess machine learning methods 

Lecture notes provided on Absalon

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 and 2 hours of exercises per week for 7 weeks.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 121
  • Practical exercises
  • 14
  • Project work
  • 42
  • Exam
  • 1
  • Total
  • 206
7,5 ECTS
Type of assessment
Oral examination, 30 minutes (30-minute preparation time)
Exam registration requirements

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

All aids allowed
  • Aids are allowed during preperation.
  • No aids during examination.
Marking scale
7-point grading scale
Censorship form
No external censorship
Internal examiners

Same as the ordinary exam.

If the the two mandatory homework assignments were not approved before the ordinary exam they must be resubmitted. They must be handed in three weeks before the re-exam and must be approved before the commencement of the re-exam.

Criteria for exam assesment

The student should convincingly and accurately demonstrate the knowledge, skills and competences described under intended learning outcome.