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Explainable AI

5 credits

The courses is for professionals and part of the programme MAISTR ( where participants can study the entire programme or individual courses. The course is part of the course track machine learning and is held online in English.
The course covers the following topics:

  • Introduction to the multidisciplinary topics of explainable AI, what is XAI, why is it important, plus related terminologies
  • Broad taxonomy of XAI methods including Intrinsic vs post hoc, model-specific vs model-agnostic, and local vs global
  • Trade-off between accuracy and explainability, human-friendly explanations,
  • Intrinsically explainable models including Linear Regression, Logistic Regression, Generalized Linear Model (GLM), Generalized Additive Model (GAM), and Decision Tree.
  • XAI methods including, Partial Dependence Plot (PDP), Conformal Prediction, Individual Conditional Expectation (ICE), Feature Importance, Saliency Maps, Local Interpretable Model-Agnostic Explanations (LIME), SHAP, Integrated Gradient (IG)
  • Evaluation of explainability

Spring 2025 (Distance (Internet), Varied, 33%)


Advanced level

Application code:


Entry requirements:

Degree of Bachelor of Science in Engineering, Computer Science and Engineering including an independent project 15 credits or Degree of Bachelor of Science with a major in Computer Science and Engineering including an independent project 15 credits or the equivalent of 180 Swedish credit points or 180 ECTS credits at an accredited university. Programming 7.5 credits and Mathematics 7.5 credits including linear algebra. Applicants must have written and verbal command of the English language equivalent to English course 6 in Swedish Upper-Secondary School. Exemption of the requirement in Swedish is granted.

Selection rules:

Credits: 100%

Start week:

week: 14

Number of gatherings:


Instructional time:

Various times

Language of instruction:

Teaching is in English.

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