NMAK23006U Interpretable Machine Learning

Volume 2025/2026
Education

MSc Programme in Actuarial Mathematics

MSc Programme in Mathematics-Economics

MSc Programme in Statistics

Content

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

Knowledge:

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

 

Skills:

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


Competences:

  • 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
Collective
Credit
7,5 ECTS
Type of assessment
Oral examination, 30 minutes
Examination prerequisites

One mandatory assignment must be approved and valid before the student is allowed attending the exam.

Aid
No aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Internal examiners
Re-exam

Same as the ordinary exam.

If the one mandatory homework assignment was not approved before the ordinary exam it must be resubmitted. The mandatory homework assignment 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.