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Duration 21 hours
Course Outline
Overview of Advanced XAI Methodologies
- Recap of foundational XAI methods
- Difficulties associated with interpreting complex AI models
- Current trends in XAI research and innovation
Model-Agnostic Explainability Approaches
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Anchor-based explanations
Model-Specific Explainability Strategies
- Layer-wise relevance propagation (LRP)
- DeepLIFT (Deep Learning Important FeaTures)
- Gradient-based techniques (Grad-CAM, Integrated Gradients)
Interpretation of Deep Learning Architectures
- Understanding convolutional neural networks (CNNs)
- Clarifying recurrent neural networks (RNNs)
- Examining transformer-based models (BERT, GPT)
Managing Interpretability Complexities
- Overcoming the limitations of black-box models
- Achieving a balance between accuracy and interpretability
- Ensuring bias mitigation and fairness in explanations
Practical XAI Applications in Real-World Scenarios
- Utilization of XAI in healthcare, finance, and legal sectors
- Adhering to AI regulatory and compliance standards
- Fostering trust and accountability through XAI
Emerging Trends in Explainable AI
- Novel techniques and tools emerging in XAI
- Next-generation explainability frameworks
- Prospects and hurdles in AI transparency
Conclusion and Future Directions
Requirements
- A robust grasp of AI and machine learning concepts
- Practical experience with neural networks and deep learning frameworks
- Basic familiarity with fundamental XAI techniques
Target Audience
- Seasoned AI researchers
- Machine learning engineers
