Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Advanced XAI Methodologies
- Recap of foundational XAI methods
- Difficulties in interpreting complex AI models
- Current trends in XAI research and development
Model-Agnostic Explainability Approaches
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Anchor-based explanations
Model-Specific Explainability Approaches
- Layer-wise relevance propagation (LRP)
- DeepLIFT (Deep Learning Important FeaTures)
- Gradient-based techniques (Grad-CAM, Integrated Gradients)
Interpreting Deep Learning Architectures
- Analysing convolutional neural networks (CNNs)
- Explaining recurrent neural networks (RNNs)
- Evaluating transformer-based models (BERT, GPT)
Navigating Interpretability Hurdles
- Overcoming the limitations of black-box models
- Striking a balance between accuracy and interpretability
- Managing bias and fairness within explanatory outputs
Real-World Applications of XAI
- XAI implementation in healthcare, finance, and legal sectors
- AI regulatory frameworks and compliance mandates
- Fostering trust and accountability via XAI
Emerging Trends in Explainable AI
- Novel techniques and tools in the XAI landscape
- Next-generation models for explainability
- Prospects and obstacles regarding AI transparency
Conclusion and Recommended Next Steps
Requirements
- A robust grasp of AI and machine learning fundamentals
- Practical experience with neural networks and deep learning
- Basic knowledge of introductory XAI techniques
Target Audience
- Veteran AI researchers
- Machine learning engineers
21 Hours
