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Stanford University

Hacking AI - Security & Privacy of Machine Learning Models

Stanford University via YouTube

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This webinar examines the stability and security of machine learning models when faced with adversarial behavior. It discusses adversarial examples and defenses, measuring resistance to attacks, and privacy topics including differential privacy.

Syllabus

Introduction
Machine Learning Pipeline
Adversary Examples
Defenses
adversarial examples
perceptual ad blocking
adversarial noise
data protection
differential privacy
accuracy
privacy
differential privacy level
transfer learning
CTML
Why cant we identify what the data said
Measuring resistance to adversarial attacks
Quantum computing

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Stanford Online

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