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Explainable deep learning models for healthcare - CDSS 3
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.
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Rating | Not enough ratings |
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Length | 5 weeks |
Effort | 5-6 hours/week |
Starts | Jun 26 (47 weeks ago) |
Cost | $49 |
From | University of Glasgow via Coursera |
Instructor | Fani Deligianni |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Science Data Science Programming |
Tags | Life Sciences Data Science Machine Learning Health Informatics |
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Rating | Not enough ratings |
---|---|
Length | 5 weeks |
Effort | 5-6 hours/week |
Starts | Jun 26 (47 weeks ago) |
Cost | $49 |
From | University of Glasgow via Coursera |
Instructor | Fani Deligianni |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Science Data Science Programming |
Tags | Life Sciences Data Science Machine Learning Health Informatics |
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