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Home » Applied Sciences » Computer Science » Electrical Engineering and Computer Science (M-I-T) » Machine Learning for Healthcare (Spring 2019) (M-I-T) » Lecture 16: Reinforcement Learning, Part 1 (M-I-T)

Lecture 16: Reinforcement Learning, Part 1 (M-I-T)

Course:Machine Learning for Healthcare (Spring 2019) (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. Dr. Peter Szolovits, Prof. Dr. David Sontag
Level:Graduate

Machine Learning for Healthcare (Spring 2019) (M-I-T)

Lecture 10: Application of Machine Learning to Cardiac Imaging (M-I-T)
Lecture 11: Differential Diagnosis (M-I-T)
Lecture 12: Machine Learning for Pathology (M-I-T)
Lecture 13: Machine Learning for Mammography (M-I-T)
Lecture 14: Causal Inference, Part 1 (M-I-T)
Lecture 15: Causal Inference, Part 2 (M-I-T)
Lecture 16: Reinforcement Learning, Part 1 (M-I-T)
Lecture 17: Reinforcement Learning, Part 2 (M-I-T)
Lecture 18: Disease Progression Modeling and Subtyping, Part 1 (M-I-T)
Lecture 19: Disease Progression Modeling and Subtyping, Part 2 (M-I-T)
Lecture 1: What Makes Healthcare Unique? (M-I-T)
Lecture 20: Precision Medicine (M-I-T)
Lecture 21: Automating Clinical Work Flows (M-I-T)
Lecture 22: Regulation of Machine Learning / Artificial Intelligence in the US (M-I-T)
Lecture 23: Fairness (M-I-T)
Lecture 24: Robustness to Dataset Shift (M-I-T)
Lecture 25: Interpretability (M-I-T)
Lecture 2: Overview of Clinical Care (M-I-T)
Lecture 3: Deep Dive Into Clinical Data (M-I-T)
Lecture 4: Risk Stratification, Part 1 (M-I-T)

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