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Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The significance of transparency in AI models
- Primary challenges in achieving AI interpretability
Fundamental XAI Techniques
- Model-agnostic methods: LIME and SHAP
- Explainability approaches specific to certain models
- Clarifying decisions made by black-box models
Practical Application of XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into simple machine learning models
- Visualizing explanations and model behavior
Challenges in Explainability
- Balancing accuracy with interpretability
- Current limitations of XAI methods
- Addressing bias and fairness in explainable models
Ethical Considerations in XAI
- Analyzing the ethical implications of AI transparency
- Reconciling explainability with model performance
- Privacy and data protection issues within XAI
Real-World Applications of XAI
- Utilizing XAI in healthcare, finance, and law enforcement
- Regulatory mandates for explainability
- Fostering trust in AI systems through transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Explaining neural networks and deep learning models
- Interpreting complex AI systems
Future Trends in Explainable AI
- Innovative techniques emerging in XAI research
- Challenges and opportunities for future AI transparency
- The influence of XAI on responsible AI development
Summary and Next Steps
Requirements
- Foundational understanding of machine learning concepts
- Proficiency in Python programming
Audience
- Individuals new to AI
- Enthusiasts of data science
14 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 3200 € + VAT*
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