Machine Learning for Healthcare
MIT's course on applying machine learning methods to clinical medicine, offered jointly through Electrical Engineering and Computer Science and Health Sciences and Technology. Lectures and readings cover the structure and quirks of clinical data (electronic health records, physiological signals, imaging), then move through risk stratification, disease progression modeling, precision medicine, automated diagnosis, and subtype discovery. Case studies examine how these models can reshape clinical workflows, alongside discussion of fairness, interpretability, and deployment challenges specific to healthcare settings. Materials on MIT OpenCourseWare include lecture notes, readings, and problem sets used in the actual MIT class, aimed at students who already have a machine learning background and want to see it applied to real clinical problems. No certificate is offered, but the full set of course materials is free to use under MIT's open license.