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

• Lecture 1: What Makes Healthcare Unique? (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)
• Lecture 5: Risk Stratification, Part 2 (M-I-T)
• Lecture 6: Physiological Time-Series (M-I-T)
• Lecture 8: Natural Language Processing (NLP), Part 2 (M-I-T)
• Lecture 9: Translating Technology Into the Clinic (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 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)