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Starts 6 June 2025 09:34

Ends 6 June 2025

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Veridical Data Science Towards Trustworthy AI

Explore the principles of veridical data science and its role in developing trustworthy AI systems with UC Berkeley's Bin Yu.
Simons Institute via YouTube

Simons Institute

2484 Courses


50 minutes

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Overview

Explore the principles of veridical data science and its role in developing trustworthy AI systems with UC Berkeley's Bin Yu.

Syllabus

  • Introduction to Veridical Data Science
  • Definition and Principles of Veridical Data Science
    Importance in AI and Machine Learning
    Overview of Statistical Thinking in Veridical Data Science
  • Foundations of Trustworthy AI
  • Characteristics of Trustworthy AI Systems
    Challenges in Building Trustworthy AI
    Real-world Applications and Case Studies
  • Data Quality and Reliability
  • Data Collection and Preprocessing
    Assessing Data Quality: Metrics and Methods
    Techniques for Ensuring Data Reliability
  • Model Transparency and Explainability
  • Importance of Model Interpretability
    Techniques for Interpretability: LIME, SHAP, etc.
    Balancing Complexity and Transparency
  • Fairness and Bias in AI
  • Identifying Bias in AI Models
    Strategies for Mitigating Bias
    Ethical Considerations in AI Development
  • Robustness and Robust Statistics
  • Definition and Importance of Robustness in AI
    Techniques for Building Robust Models
    Evaluating Robustness: Stress Testing and Adversarial Examples
  • Reproducibility in Data Science
  • Importance of Reproducibility in Research
    Best Practices for Reproducible Workflows
    Tools and Frameworks for Reproducibility
  • AI Safety and Security
  • Identifying Security Risks in AI Systems
    Techniques for Building Secure AI Models
    Response Strategies for AI Failures
  • Evaluation and Validation of AI Systems
  • Frameworks for Evaluating AI Performance
    Validation Techniques: Cross-Validation, A/B Testing
    Continuous Monitoring and Feedback Systems
  • Future Directions for Veridical Data Science
  • Innovations in Trustworthy AI Techniques
    The Role of Policy and Regulation
    Envisioning the Future of Veridical Data Science and AI
  • Course Wrap-up and Final Project
  • Reviewing Key Concepts and Ideas
    Final Project: Designing a Trustworthy AI System
    Discussion of Real-World Implementations and Career Applications

Subjects

Computer Science