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Course Outline
Introduction to Advanced XAI Techniques
- Review of basic XAI methods
- Challenges in interpreting complex AI models
- Trends in XAI research and development
Model-Agnostic Explainability Techniques
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Anchor explanations
Model-Specific Explainability Techniques
- Layer-wise relevance propagation (LRP)
- DeepLIFT (Deep Learning Important FeaTures)
- Gradient-based methods (Grad-CAM, Integrated Gradients)
Explaining Deep Learning Models
- Interpreting convolutional neural networks (CNNs)
- Explaining recurrent neural networks (RNNs)
- Analyzing transformer-based models (BERT, GPT)
Handling Interpretability Challenges
- Addressing black-box model limitations
- Balancing accuracy and interpretability
- Dealing with bias and fairness in explanations
Applications of XAI in Real-World Systems
- XAI in healthcare, finance, and legal systems
- AI regulation and compliance requirements
- Building trust and accountability through XAI
Future Trends in Explainable AI
- Emerging techniques and tools in XAI
- Next-generation explainability models
- Opportunities and challenges in AI transparency
Summary and Next Steps
Requirements
- Solid understanding of AI and machine learning
- Experience with neural networks and deep learning
- Familiarity with basic XAI techniques
Audience
- Experienced AI researchers
- Machine learning engineers
21 Hours
