Electrical Engineering and Computer Science
Machine Learning for Healthcare
Due to technical difficulties with the original video, lecture 15 is from the spring 2020 version of the course.
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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