Machine Learning for Healthcare | Electrical Engineering and Computer Science | MIT OpenCourseWare

Lecture Videos

Lecture 1: What Makes Healthcare Unique?

Lecture 2: Overview of Clinical Care

Lecture 3: Deep Dive Into Clinical Data

Lecture 4: Risk Stratification, Part 1

Lecture 5: Risk Stratification, Part 2

Lecture 6: Physiological Time-Series

Lecture 7: Natural Language Processing (NLP), Part 1

Lecture 8: Natural Language Processing (NLP), Part 2

Lecture 9: Translating Technology Into the Clinic

Lecture 10: Application of Machine Learning to Cardiac Imaging

Lecture 11: Differential Diagnosis

Lecture 12: Machine Learning for Pathology

Lecture 13: Machine Learning for Mammography

Lecture 14: Causal Inference, Part 1

Lecture 15: Causal Inference, Part 2

Lecture 16: Reinforcement Learning, Part 1

Lecture 17: Reinforcement Learning, Part 2

Lecture 18: Disease Progression Modeling and Subtyping, Part 1

Lecture 19: Disease Progression Modeling and Subtyping, Part 2

Lecture 20: Precision Medicine

Lecture 21: Automating Clinical Work Flows

Lecture 22: Regulation of Machine Learning / Artificial Intelligence in the US

Lecture 23: Fairness

Lecture 24: Robustness to Dataset Shift

Lecture 25: Interpretability

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Spring 2019

Level

Graduate

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