Introduction
Explainable AI seeks to make machine learning models transparent, interpretable, and understandable to humans. This course explores practical tools and theoretical foundations for interpreting complex models. Participants will learn both global and local explanation methods and how to apply them responsibly. Real-world examples show how explainability improves trust and regulatory compliance. By the end, learners will be able to evaluate and explain AI decisions.
Course Objectives
- Understand the importance of interpretability
- Learn model-agnostic explanation techniques
- Explore interpretability for deep learning models
- Apply XAI tools to real tasks
- Understand regulatory requirements for transparency
Target Audience
- ML engineers
- Data scientists
- Product managers
- Compliance officers
- Students studying AI ethics
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
Expand all sectionsCollapse all sections
- Day 1: Explainability Foundations• Why models need to be explained
• Interpretable vs. black-box models
• Types of explanations
• Key challenges
• Hands-on: Basic model interpretation0 - Day 2: Model-Agnostic Techniques• LIME
• SHAP
• Partial dependence plots
• Feature importance
• Hands-on: Apply XAI tools0 - Day 3: Interpreting Deep Models• Activation visualization
• Attention mechanisms
• Saliency maps
• Model introspection
• Hands-on: Vision/NLP interpretability0 - Day 4: Industry & Regulatory Requirements• GDPR & transparency
• Model governance
• User-facing explanations
• Risk management
• Case studies0 - Day 5: Building Explainable Systems• Designing interpretable workflows
• Human-in-the-loop decision systems
• Testing explanations
• Future of XAI
• Capstone project0
Instructor
Lessons:
