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Starts 6 June 2025 09:34
Ends 6 June 2025
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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
- Foundations of Trustworthy AI
- Data Quality and Reliability
- Model Transparency and Explainability
- Fairness and Bias in AI
- Robustness and Robust Statistics
- Reproducibility in Data Science
- AI Safety and Security
- Evaluation and Validation of AI Systems
- Future Directions for Veridical Data Science
- Course Wrap-up and Final Project
Definition and Principles of Veridical Data Science
Importance in AI and Machine Learning
Overview of Statistical Thinking in Veridical Data Science
Characteristics of Trustworthy AI Systems
Challenges in Building Trustworthy AI
Real-world Applications and Case Studies
Data Collection and Preprocessing
Assessing Data Quality: Metrics and Methods
Techniques for Ensuring Data Reliability
Importance of Model Interpretability
Techniques for Interpretability: LIME, SHAP, etc.
Balancing Complexity and Transparency
Identifying Bias in AI Models
Strategies for Mitigating Bias
Ethical Considerations in AI Development
Definition and Importance of Robustness in AI
Techniques for Building Robust Models
Evaluating Robustness: Stress Testing and Adversarial Examples
Importance of Reproducibility in Research
Best Practices for Reproducible Workflows
Tools and Frameworks for Reproducibility
Identifying Security Risks in AI Systems
Techniques for Building Secure AI Models
Response Strategies for AI Failures
Frameworks for Evaluating AI Performance
Validation Techniques: Cross-Validation, A/B Testing
Continuous Monitoring and Feedback Systems
Innovations in Trustworthy AI Techniques
The Role of Policy and Regulation
Envisioning the Future of Veridical Data Science and AI
Reviewing Key Concepts and Ideas
Final Project: Designing a Trustworthy AI System
Discussion of Real-World Implementations and Career Applications
Subjects
Computer Science