Explainable Deep Learning Models for Healthcare
By External Provider
About this course
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 explanations such as Class-Activation Mapping (CAM) and Gradient-Weighted CAM are explained and implemented. The learners will understand axiomatic attributions and why they are important. Finally, attention mechanisms are going to be incorporated after Recurrent Layers and the attention weights will be visualised to produce local explanations of the model.
What you will learn
- Program global explainability methods in time-series classification.
- Program local explainability methods for deep learning such as CAM and GRAD-CAM.
- Understand axiomatic attributions for deep learning networks.
- Incorporate attention in Recurrent Neural Networks and visualise the attention weights.