Course objectives

This course aims to provide advanced knowledge on interpretable and explainable machine learning approaches for doctoral students utilizing the techniques in their research. These students must be able to comprehend AI decisions to advance their research further. The knowledge gained from this course will empower students to apply the techniques to their own research, allowing for better analysis and understanding of their learning systems

Syllabus

Syllabus: Interpretable and Explainable Machine Learning (PDF)

Course credits

5 credits

Course code

TS00027

Language of instruction

English

Entry requirements

Students must be admitted to doctoral studies

Course dates

The course will run in November 2026 at the following dates:

  • Tuesday 3 November, 10.15 -15.00
  • Wednesday 4 November, 10.15 -15.00
  • Thursday 5 November, 10.15 -15.00
  • Monday 9 November, 10.15-15.00
  • Wednesday 11 November, 10.15-15.00
  • Thursday 12 November, 10.15-15.00

Teaching mode

The course will have a hybrid format

Application

The application is closed

Contact

For academic questions: Reza Khoshkangini

For administrative questions: Kristina Olsson