Content
- Goals and challenges of explainability for computer vision
- Interpretability of classical machine learning models
- Global vs. local explanations
- Post hoc explanations
- Intrinsically explainable neural networks
- Evaluation of explanations
- Visualization techniques
- Applications of explanations
- XAI beyond classification