6.S897 | Spring 2019 | Graduate

Machine Learning for Healthcare

Lecture Notes

Ses # Lecture Slides Lecture Notes

1

Lecture 1: Introduction: What Makes Healthcare Unique? slides (PDF - 2.4MB)

Lecture 1 Notes (PDF)

2

Lecture 2: Overview of Clinical Care slides (PDF - 2.6MB)

Lecture 2 Notes (PDF)

3

Lecture 3: Deep Dive into Clinical Data slides (PDF - 2.1MB)

Lecture 3 Notes (PDF - 1.5MB)

4

Lecture 4: Risk Stratification, Part 1 slides (PDF - 1.2MB)

Lecture 4 Notes (PDF)

5

Lecture 5: Risk Stratification, Part 2 slides (PDF - 1.9MB)

Lecture 5 Notes (PDF)

6

Lecture 6: Physiological Time-Series slides (PDF - 1.4MB)

Lecture 6 Notes (PDF)

7

Lecture 7: Natural Language Processing (NLP), Part 1 slides (PDF - 1MB)

Lecture 7 Notes (PDF)

8

Lecture 8: Natural Language Processing (NLP), Part 2 slides (PDF - 2.0MB)

Lecture 8 Notes (PDF)

9

Lecture 9: Translating Technology into the Clinic slides (PDF)

Lecture 9 Notes (PDF)

10

Lecture 10: Machine Learning for Cardiology slides (PDF - 3.9MB)

Lecture 10 Notes (PDF - 1.3MB)

11

Lecture 11: Machine Learning for Differential Diagnosis slides (PDF - 1.9MB)

Lecture 11 Notes (PDF)

12

Lecture 12: Machine Learning for Pathology slides (PDF - 6.8MB)

Lecture 12 Notes (PDF)

13

Lecture 13: Machine Learning for Mammography slides (PDF - 2.2MB)

Lecture 13 Notes (PDF)

14

Lecture 14: Causal Inference, Part 1 slides (PDF - 2MB)

Lecture 14 Notes (PDF)

15

Lecture 15: Causal Inference, Part 2 slides (PDF)

Lecture 15 Notes (PDF)

16

Lecture 16: Reinforcement Learning slides (PDF)

Lecture 16 Notes (PDF)

17

Lecture 17: Evaluating Dynamic Treatment Strategies slides (PDF)

Lecture 17 Notes (PDF)

18

Lecture 18: Disease Progression & Subtyping, Part 1 slides (PDF)

Lecture 18 Notes (PDF)

19

Lecture 19: Disease Progression & Subtyping, Part 2 slides (PDF - 2.5MB)

Lecture 19 Notes (PDF)

20

Lecture 20: Precision Medicine slides (PDF - 1.6MB)

Lecture 20 Notes (PDF)

21

Lecture 21: Automating Clinical Workflows slides (PDF - 1.6MB)

Lecture 21 Notes (PDF)

22

Lecture 22.1: Regulation of ML/AI in the US slides (PDF - 1.4MB)

Lecture 22.2: Human Subjects Research slides (PDF)

Lecture 22 Notes (PDF)

23

Lecture 23: Fairness slides (PDF - 1.5MB)

No provided notes

24

Lecture 24: Robustness to Dataset Shift slides (PDF - 2.2MB)

Lecture 24 Notes (PDF)

25

Lecture 25: Interpretability slides (PDF - 3.2MB)

No provided notes

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