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Starts 6 June 2025 12:05
Ends 6 June 2025
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44 minutes
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Overview
Explore prediction-powered inference methods with Anastasios Angelopoulos, focusing on theoretical aspects of trustworthy AI and statistical techniques for reliable predictions.
Syllabus
- Introduction to Trustworthy AI
- Basics of Prediction-Powered Inference
- Theoretical Foundations
- Techniques for Reliable Predictions
- Advanced Prediction-Powered Methods
- Trustworthy AI Development
- Evaluation of Trustworthy AI
- Practical Implementation
- Future Trends in Trustworthy AI
- Course Review and Applications
Overview of AI trustworthiness
Importance of reliability in AI predictions
Definition and key concepts
Historical context and development
Statistical inference fundamentals
Predictive accuracy and reliability metrics
Ensemble methods
Bayesian inference
Robust statistics
Case studies and real-world applications
Limitations and challenges
Ethical considerations
Design principles for trustworthy systems
Frameworks for assessing AI reliability
Case studies in various domains
Tools and libraries for prediction-powered inference
Hands-on project: Building a trustworthy predictive model
Emerging research directions
Impacts on industry and society
Recap of key concepts
Discussion on implementing course learnings in practice
Subjects
Computer Science
