Seminars
1. Course Organization and Motivation for Explainable AI — Introductory Seminar by Instructor
2. Interpretability and Explainability: Concepts and Taxonomies
3. Intrinsically Interpretable Machine Learning Models
4. Model-Agnostic Local Explanation Methods
5. Feature Attribution Methods for Deep Learning
6. Global Explanation and Surrogate Models
7. Concept-Based Explainability
8. Counterfactual Explanations
9. Robustness, Stability, and Faithfulness of Explanations
10. Evaluation of Explainable AI Methods
11. Explainability for Deep Learning and Foundation Models
12. Explainability in Automated Machine Learning Systems
13. Explainable AI, Trustworthy AI, and Regulation
14. Student Project Presentations
15. Open Research Challenges and Future Directions in XAI