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.
Duration 14 hours
Course Outline
Introduction to Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI models
- Primary challenges in AI interpretability
Foundational XAI Techniques
- Model-agnostic approaches: LIME, SHAP
- Explainability methods specific to certain models
- Interpreting decisions from black-box models
Practical Application of XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into simple machine learning models
- Visualising explanations and model behaviours
Challenges in Explainability
- Balancing accuracy against interpretability
- Current limitations of XAI methodologies
- Addressing bias and ensuring fairness in explainable models
Ethical Perspectives on XAI
- Navigating the ethical implications of AI transparency
- Striking a balance between explainability and model performance
- Managing privacy and data protection in the context of XAI
Real-World Uses of XAI
- Applications in healthcare, finance, and law enforcement
- Compliance with regulatory standards for explainability
- Establishing trust in AI systems through transparency
Advanced Concepts in XAI
- Investigating counterfactual explanations
- Explaining neural networks and deep learning architectures
- Interpreting intricate AI systems
Future Trajectories in Explainable AI
- Emerging innovations in XAI research
- Future challenges and opportunities in AI transparency
- The influence of XAI on responsible AI development
Requirements
- A solid grasp of fundamental machine learning concepts
- Proficiency in Python programming
Target Audience
- Individuals new to AI
- Data science enthusiasts
