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22. Regulation of Machine Learning / Artificial Intelligence in the US
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Classroom Contents
Machine Learning for Healthcare
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- 1 1. What Makes Healthcare Unique?
- 2 2. Overview of Clinical Care
- 3 3. Deep Dive Into Clinical Data
- 4 4. Risk Stratification, Part 1
- 5 5. Risk Stratification, Part 2
- 6 6. Physiological Time-Series
- 7 7. Natural Language Processing (NLP), Part 1
- 8 8. Natural Language Processing (NLP), Part 2
- 9 9. Translating Technology Into the Clinic
- 10 10. Application of Machine Learning to Cardiac Imaging
- 11 11. Differential Diagnosis
- 12 12. Machine Learning for Pathology
- 13 13. Machine Learning for Mammography
- 14 14. Causal Inference, Part 1
- 15 15. Causal Inference, Part 2
- 16 16. Reinforcement Learning, Part 1
- 17 17. Reinforcement Learning, Part 2
- 18 18. Disease Progression Modeling and Subtyping, Part 1
- 19 19. Disease Progression Modeling and Subtyping, Part 2
- 20 20. Precision Medicine
- 21 21. Automating Clinical Work Flows
- 22 22. Regulation of Machine Learning / Artificial Intelligence in the US
- 23 23. Fairness
- 24 24. Robustness to Dataset Shift
- 25 25. Interpretability
