MIT 6.S897 Machine Learning for Healthcare, Spring 2019
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What you'll learn
This course includes
- 31 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 25 lessons • 31 hours of video
MIT 6.S897 Machine Learning for Healthcare, Spring 2019
25 lessons
• 31 hours
MIT 6.S897 Machine Learning for Healthcare, Spring 2019
25 lessons
• 31 hours
- 1. What Makes Healthcare Unique? 01:10:42
- 2. Overview of Clinical Care 01:20:08
- 3. Deep Dive Into Clinical Data 01:23:27
- 4. Risk Stratification, Part 1 01:12:45
- 5. Risk Stratification, Part 2 01:20:08
- 6. Physiological Time-Series 01:21:01
- 7. Natural Language Processing (NLP), Part 1 01:15:38
- 8. Natural Language Processing (NLP), Part 2 01:23:24
- 9. Translating Technology Into the Clinic 01:22:46
- 10. Application of Machine Learning to Cardiac Imaging 01:21:23
- 11. Differential Diagnosis 01:20:16
- 12. Machine Learning for Pathology 55:34
- 13. Machine Learning for Mammography 41:15
- 14. Causal Inference, Part 1 01:18:43
- 15. Causal Inference, Part 2 01:02:17
- 16. Reinforcement Learning, Part 1 01:17:08
- 17. Reinforcement Learning, Part 2 55:13
- 18. Disease Progression Modeling and Subtyping, Part 1 01:21:11
- 19. Disease Progression Modeling and Subtyping, Part 2 01:12:29
- 20. Precision Medicine 01:24:51
- 21. Automating Clinical Work Flows 01:20:27
- 22. Regulation of Machine Learning / Artificial Intelligence in the US 01:21:18
- 23. Fairness 01:17:55
- 24. Robustness to Dataset Shift 01:15:16
- 25. Interpretability 01:18:42
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