Visualization Best Practices for Explainable AI

Overview

Explore machine learning visualization techniques for explainable AI across industries, covering model understanding, data analysis, and performance evaluation using Jupyter, Python, and Power BI.

Syllabus

• Introduction to Explainable AI (XAI)
Overview of Explainable AI and its importance
Key goals and challenges of XAI
Use cases across various industries
• Tools for Visualization in AI
Introduction to Jupyter Notebooks
Using Python libraries (Matplotlib, Seaborn, Plotly)
Overview of Power BI for data visualization
• Visualization Techniques for Model Understanding
Visualizing decision boundaries
Feature importance and partial dependence plots
SHAP and LIME for model interpretability
• Data Analysis for Explainable AI
Descriptive statistics and insights
Correlation matrices and heatmaps
Dimensionality reduction techniques (PCA, t-SNE)
• Evaluating Model Performance
Confusion matrices and classification reports
ROC curves and AUC
Visualizing model performance over time
• Case Studies and Applications
Visualization in healthcare AI: explaining models for patient data
Finance industry use cases: model transparency in credit scoring
Explainable AI for autonomous systems
• Ethical Considerations and Best Practices
Ensuring transparency and fairness in AI models
Avoiding pitfalls and biases in visualization
Effective communication of AI results to stakeholders
• Hands-on Project and Workshops
Guided project using Jupyter and Python for XAI
Building dashboards and reports with Power BI
Peer reviews and collaborative exercises
• Summary and Future Trends
Recap of key concepts and tools
Emerging trends in AI visualization and explainability
Resources for continued learning and development

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Subjects

Conference Talks