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...
By Fani Deligianni on Coursera
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.
Topics Covered
Frequently Asked Questions
How much does Explainable Deep Learning Models for Healthcare cost?
Visit the Explainable Deep Learning Models for Healthcare course page for current pricing and available discounts.
Who teaches Explainable Deep Learning Models for Healthcare?
Explainable Deep Learning Models for Healthcare is taught by Fani Deligianni, University of Glasgow .
What skill level is Explainable Deep Learning Models for Healthcare for?
This course is designed for all levels learners.
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