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Classroom Contents
Practical Defenses Against Adversarial Machine Learning
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- 1 Intro
- 2 Who am I
- 3 Research vs Deployment
- 4 Bad Inputs
- 5 Email Filtering
- 6 Transportation Prediction
- 7 Recommendation Engines
- 8 Trading Bots
- 9 Model Leakage
- 10 Block Lists
- 11 Multiple Signals
- 12 Raw Statistics
- 13 Conclusion
- 14 Recommendations
- 15 QA
- 16 Open Source Projects
- 17 Partial Homomorphic
- 18 Federated Learning
- 19 Incomplete Data
- 20 Contact
- 21 Vendor Examples
- 22 Deep Fakes vs Defects
- 23 Larger Models
- 24 Deep Fakes
- 25 Outro
