Explaining Image Classifiers with SHAP

As deep learning models for image classification become more complex, understanding why a model made a specific decision is critical for trust and safety. This text-based course demystifies explainable AI using SHAP to peer inside the black box of computer vision. You will transition from treating image classifiers as mysterious systems to confidently auditing and explaining their decisions. By learning the mathematical foundations of cooperative game theory and applying them to modern neural networks, you will read and write clean Python code to identify exactly which image regions drive your model's predictions. What you'll learn: - Understand the foundational principles of cooperative game theory and Shapley values. - Explain how SHAP adapts these mathematical concepts to attribute feature importance in machine learning. - Apply SHAP algorithms to popular image classification models to isolate key predictive regions. - Interpret text-based representations of SHAP outputs to evaluate model reliability and bias. - Implement modern Python workflows using the SHAP library alongside standard deep learning frameworks. - Analyze common failure modes and limitations of explainability tools in computer vision. The course begins with essential terminology, establishing a solid foundation in explainability concepts and mathematical theory before moving into practical, structured code-based implementations. You will read through step-by-step breakdowns of SHAP calculations and learn how to interpret model explanations systematically. This course is designed for beginners to machine learning interpretability, requiring only a basic familiarity with Python and fundamental machine learning concepts. Start reading today to make your computer vision models transparent and trustworthy.