Veridical Data Science Towards Trustworthy AI

Overview

Delve into the realm of veridical data science with esteemed expert Bin Yu from UC Berkeley. Discover how foundational principles of data science can be applied to develop trustworthy AI systems.

Ideal for enthusiasts and professionals in Artificial Intelligence and Computer Science, this course offers valuable insights and knowledge to advance your understanding of reliable AI solutions.

Syllabus

• Introduction to Veridical Data Science
Definition and relevance
Historical context and evolution
Overview of the course structure
• Principles of Veridical Data Science
Clarity and transparency
Predictability and reliability
Reproducibility and replicability
• Data Collection and Preprocessing
Data quality and integrity
Handling bias in data
Data cleaning techniques
• Statistical Inference for Trustworthy AI
Concepts of statistical inference
Ensuring validity and reliability
Managing uncertainty
• Machine Learning for Trustworthy AI
Understanding model interpretability
Balancing complexity and simplicity
Avoiding overfitting and underfitting
• Model Evaluation and Validation
Performance metrics and evaluation
Cross-validation techniques
Bias-variance trade-off
• Ethical Considerations and Bias Mitigation
Identifying and mitigating algorithmic bias
Ensuring fairness and equity
Ethical frameworks in AI
• Case Studies and Real-World Applications
Analysis of successful deployment of trustworthy AI systems
Lessons learned from failures
Exemplars of veridical data science
• Tools and Technologies for Veridical Data Science
Software platforms and libraries
Data visualization techniques
Open-source tools and resources
• Future Trends in Trustworthy AI
Emerging challenges and opportunities
Innovations in veridical data science
Next steps for research and development
• Course Review and Capstone Project
Integration of learned concepts
Project design and implementation
Student presentations and feedback

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Subjects

Computer Science