Trust and Distrust in ML: Privacy, Verification and Robustness - Part 2

Overview

Join us for the second part of this fascinating journey into the heart of machine learning, focusing on privacy, verification, and robustness issues. Presented by the esteemed Shafi Goldwasser, this session delves deeply into the critical trust challenges encountered in ML systems.

Ideal for those interested in artificial intelligence and computer science, this course is provided through YouTube.

Syllabus

• Introduction to Trust and Distrust in Machine Learning
Overview of trust issues in ML systems
Key concepts of privacy, verification, and robustness
• Privacy in Machine Learning
Differential privacy
Data anonymization techniques
Privacy-preserving machine learning models
Case studies on privacy failures
• Verification in Machine Learning
Formal verification methods for ML systems
Testing and validating machine learning models
Tools and techniques for model verification
Real-world applications and scenarios
• Robustness in Machine Learning
Adversarial attacks and defenses
Robustness testing and evaluation
Designing robust ML systems
Case studies on robustness challenges
• Case Studies and Applications
Examination of high-impact case studies
Lessons learned from trust issues in past projects
• Expert Guest Lecture: Shafi Goldwasser
Deep dive into specific trust challenges
Open Q&A session
• Future Directions in Trust for ML
Emerging trends and research areas
Discuss unresolved challenges and potential solutions
• Conclusion and Wrap-up
Summary of key learnings
Final thoughts on building trustworthy ML systems
• Project: Building a Trustworthy ML Application
Design and implement a small-scale ML system addressing privacy, verification, and robustness
Present and critique projects in a peer review session

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Subjects

Computer Science