Interpretable Machine Learning

Course content

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

  • Introduction of various machine learning methods. Topics may include but are not limited to: additive models, tree based methods, neural networks, multivariate adaptive splines
  • Discussoion on interpretability. Various topics on interpretable machine learning, including Partial Dependence Plots (PDP), Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP) values.



MSc Programme in Actuarial Mathematics

MSc Programme in Mathematics-Economics

MSc Programme in Statistics

Learning outcome


  • Regression & Classification with machine learning methods
  • Various machine learning interpretation methods
  • Understand the innerworking and limitations of those methods



A general ability to use and the select the right machine learning method to solve practical problems


  • Use R relating to the course area

4 hours of lectures and 2 hours of exercises per week for 7 weeks.

Lecture notes 

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.

7,5 ECTS
Type of assessment
Oral examination, 30 min under invigilation
Type of assessment details
30min oral examination with 30min 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.

Marking scale
7-point grading scale
Censorship form
No external censorship
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 handed in three weeks before the re-exam and must be approved before the commencement of the re-exam.

Criteria for exam assessment

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

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 110
  • Practical exercises
  • 14
  • Project work
  • 42
  • Exam
  • 12
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 3
No limit.
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Munir Eberhardt Hiabu   (2-706b437064776b316e7831676e)
Saved on the 28-02-2023

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