Visualization Best Practices for Explainable AI

Overview

Join us as we explore cutting-edge visualization techniques in machine learning, tailored for explainable AI. This immersive experience covers crucial aspects such as model understanding, comprehensive data analysis, and detailed performance evaluation.

Our focus will be on leveraging tools like Jupyter, Python, and Power BI, essential for both beginners and seasoned professionals.

Suitable for industry experts and enthusiasts alike, this session offers invaluable insights into how these technologies revolutionize AI explainability across various sectors. Don’t miss this chance to enhance your skill set and stay ahead in the rapidly evolving field of artificial intelligence.

Take advantage of this opportunity provided by YouTube and presented within the categories of Artificial Intelligence Courses and Conference Talks.

Engage with experts and peers, deepening your understanding of AI visualization practices.

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