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Course Outline
Introduction to Explainable AI (XAI) and Model Transparency
- What constitutes Explainable AI?
- The importance of transparency in AI systems
- Interpretability versus performance in AI models
Overview of XAI Techniques
- Model-agnostic methods: SHAP and LIME
- Explainability techniques tailored to specific models
- Explaining neural networks and deep learning architectures
Developing Transparent AI Models
- Practical implementation of interpretable models
- Comparing transparent models against black-box approaches
- Balancing complexity with explainability
Advanced XAI Tools and Libraries
- Utilising SHAP for model interpretation
- Leveraging LIME for local explainability
- Visualising model decisions and behaviours
Tackling Fairness, Bias, and Ethical AI
- Identifying and mitigating bias in AI models
- Fairness in AI and its broader societal impacts
- Ensuring accountability and ethics during AI deployment
Real-World Applications of XAI
- Case studies within healthcare, finance, and government sectors
- Interpreting AI models for regulatory compliance
- Building trust through transparent AI systems
Future Directions in Explainable AI
- Emerging research trends in XAI
- Challenges associated with scaling XAI for large-scale systems
- Opportunities shaping the future of transparent AI
Summary and Next Steps
Requirements
- Experience in machine learning and AI model development
- Proficiency in Python programming
Audience
- Data scientists
- Machine learning engineers
- AI specialists
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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