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Master Explainable AI: Interpreting Image Classifier Decisions

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1.Introduction to Explainable AI

Course Overview Explainable AI Primer on Gradients Coding Exercise on Derivatives Introductory Quiz on Explainable AI

Project

Explainable AI: The Power of Interpreting ML Models

2.Saliency Maps

Introduction to Saliency Maps—The Vanilla Gradient Guided Backpropagation Smooth Gradient Saliency Integrated Gradients LIME Layer-wise Relevance Propagation LRP-γ and LRP-Ɛ Rules SHAP Coding Exercise on Saliency Maps Quiz on Saliency Maps

3.Class Activation Maps

Introduction to Class Activation Maps GradCAM X-GradCAM Eigen-CAM Coding Exercise on Class Activation Maps Quiz on Class Activation Maps

4.Miscellaneous Methods

Counterfactual Explanations Perturbation-Based Explanations Concept Explanations Prototypical Explanations Coding Exercise on Miscellaneous Methods Quiz on Miscellaneous Methods

5.Metrics of Interpretability

Ground-Truth Faithfulness—Feature Agreement Ground-Truth Faithfulness—Rank Correlation Predictive Faithfulness Stability and Fairness Coding Exercise on Metrics of Interpretability Quiz on Metrics of Interpretability Course Conclusion

Assessment

Final Assessment

Mini Project

Weakly Supervised Object Localization

Master Explainable AI: Interpreting Image Classifier Decisions

Quiz on Metrics of Interpretability

Quiz yourself on the concepts learned in this chapter.

We'll cover the following...

Technical Quiz

1.

Given a post-hoc explanation $S = [0.1, 0.4, -0.6, 0.7, 0.9, -0.5]$ and the ground-truth explanation $S_g = [0.2, 0.3, -0.4, 0.9, 0.4, -0.5]$ , what is the Top-5 feature agreement metric between them?

A.

0.2

B.

0.8

C.

0.6

D.

0.7

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