Learning Path

Interpretable and Explainable AI (XAI)

Machine Learning Fundamentals → Evaluating and Trusting Explanations

11 Knowledge Nodes · 11 Learning Steps · v0 Active

This path equips advanced students and practitioners with the knowledge to make AI models interpretable. It covers interpretable models, model-agnostic methods like LIME and SHAP, attention visualization, and counterfactual explanations, grounded in necessary ML and deep learning prerequisites.

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11 nodes18 edges3 relationships

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Learning Route

11 learning steps · 3 phases. Click any step to inspect it and see it on the Knowledge Map.

Phase 1

Foundation

4 steps

Phase 2

Core Concepts

4 steps

Phase 3

Advanced Topics

3 steps