Explainable Deep Learning Models for Healthcare

This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification. Subsequently, model-specific explan

• Program global explainability methods for time-series
• Program local explainability methods such as CAM and GRAD-CAM
• Understand axiomatic attributions for deep networks
• Incorporate attention in RNNs and visualize attention weights