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HST 956
Machine Learning For Healthcare

Term: Spring

Course Director(s): David Sontag, Peter Szolovits
Time:
T/Th 2:30-4pm
Location:
MIT: 4-270
Course Website:
None
Prerequisite:
6.034, 6.438, 6.036, 6.806, 6.867, or 9.520
Restrictions:
Limited to 55
MIT Units:
3-0-9 (G-Level Credit)
Harvard Units:
Unknown
Introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows. Topics include causality, interpretability, algorithmic fairness, time-series analysis, graphical models, deep learning and transfer learning. Guest lectures by clinicians from the Boston area and course projects with real clinical data emphasize subtleties of working with clinical data and translating machine learning into clinical practice.