Explainable Deep Learning Models for Healthcare
Key Takeaways
Explains interpretability and explainability in machine learning applications using Permutation Feature Importance, Local Interpretable Model-agnostic Explanations, and SHapley Additive exPlanation
Original Description
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.
Watch on External: Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: ML Maths Basics
View skill →Related Reads
📰
📰
📰
📰
Understanding Deep Learning Through Four Interactive Experiments
Medium · Data Science
Understanding Deep Learning Through Four Interactive Experiments
Medium · Deep Learning
Optimizers in Deep Learning: From Gradient Descent to Adam
Medium · Deep Learning
The Meta-Architecture of Interface Fracture: High-Dimensional Logical Stress and Systemic Collapse…
Medium · Deep Learning
🎓
Tutor Explanation
DeepCamp AI