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Duration 14 hours
Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI models
- Major challenges in AI interpretability
Foundational XAI Techniques
- Model-agnostic approaches: LIME and SHAP
- Explainability methods specific to certain models
- Interpreting decisions from black-box models
Practical XAI Tool Usage
- Overview of open-source XAI libraries
- Applying XAI to simple machine learning models
- Visualizing model explanations and behavior
Explainability Challenges
- Balancing accuracy against interpretability
- Constraints of existing XAI methods
- Addressing bias and fairness in explainable models
Ethical Dimensions of XAI
- Evaluating the ethical impact of AI transparency
- Striking a balance between explainability and performance
- Navigating privacy and data protection in XAI
Practical Applications of XAI
- Use cases in healthcare, finance, and law enforcement
- Regulatory standards for model explainability
- Fostering trust in AI systems via transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Explaining neural networks and deep learning architectures
- Interpreting complex AI systems
Future Directions in Explainable AI
- New techniques in XAI research
- Opportunities and hurdles for future AI transparency
- The role of XAI in responsible AI development
Requirements
- A foundational understanding of machine learning principles
- Proficiency with Python programming
Target Audience
- AI beginners
- Enthusiasts in data science
Testimonials (1)
The details of the mathematical formulas that highlight the biases and limitations as well as the important parameters to consider for the practical application of LIME and SHAPE methods.
Heddy Bouron Cardey - Bayer
Course - Introduction to Explainable AI (XAI) for Beginners
Machine Translated
