This course is an introduction to eXplainable Artificial Intelligence (XAI). In this course the learner will:
- understand the importance of interpretability
- discover the existing methods, in particular perturbation features importance, LIME, SHAP, FGC and Grad-CAM
- put your hands on three tutorials to uncover how to interpret the outputs and graphs of those methods
- learn to chose which method is suitable for your specific task
The course is helded online on September 22nd from 2 pm to 6 pm
| Time | Content |
|---|---|
| 14.00 - 14.20 | Introduction to XAI |
| 14.20 - 16.50 | Tutorial on XAI model-agnostic methods |
| 16.50 - 17.00 | Break |
| 17.00 - 18.00 | Tutorials on “XAI in deep learning-based image analysis” and “XAI for Random Forests with FGC” |
| 18.00 - 18.05 | Wrap-up and conclusions |