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 Explainable AI
- Defining Explainable AI (XAI)
- The significance of transparency in AI models
- Key challenges in AI interpretability
Foundational XAI Techniques
- Model-agnostic methods: LIME, SHAP
- Model-specific explainability methods
- Explaining decisions derived from black-box models
Practical Application of XAI Tools
- Introduction to open-source XAI libraries
- Implementing XAI in simple machine learning models
- Visualising explanations and model behaviour
Challenges in Explainability
- Trade-offs between accuracy and interpretability
- Limitations of current XAI methods
- Addressing bias and fairness in explainable models
Ethical Considerations in XAI
- Understanding the ethical implications of AI transparency
- Balancing explainability with model performance
- Privacy and data protection concerns in XAI
Real-World Applications of XAI
- XAI in healthcare, finance, and law enforcement
- Regulatory requirements for explainability
- Building trust in AI systems through transparency
Advanced XAI Concepts
- Exploring counterfactual explanations
- Explaining neural networks and deep learning models
- Interpreting complex AI systems
Future Trends in Explainable AI
- Emerging techniques in XAI research
- Challenges and opportunities for future AI transparency
- Impact of XAI on responsible AI development
Summary and Next Steps
Requirements
- Fundamental understanding of machine learning concepts
- Proficiency in Python programming
Target Audience
- AI newcomers
- Data science enthusiasts
14 Hours
