Who is this course for?
This course provides a broad introduction to explainable AI (XAI) and is intended for people with a background in computer science.
What will you learn from this course?
Students will discover and implement various explainable AI (XAI) methods, including model-specific and model-agnostic methods. They will learn about the trade-off between different aspects of XAI, such as “model performance” vs. “model explainability”. Students will also learn how to evaluate the XAI methods and the quality of the explanations according to various criteria.
What is the format for this course?
Instruction type: Teaching is in English and fully online. It consists of live lectures (+ recorded videos) and Labs in Python.
Frequency: The course consists of weekly lectures and assignments distributed over approximately two months
Examination: Exams will consist of labs and a written exam. The overall grade of Pass/Fail is awarded for this course.