Explainable AI

Education facts

Code: DT8060

The courses is for professionals and part of the programme MAISTR (hh.se/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

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. English 6 or English level 2. Exemption of the requirement in Swedish is granted for those with foreign grades.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Explainable AI

  Halmstad University

  Start date: 30 March 2026

  End date: 7 June 2026

  Pace of study: 33 %