About This Course

MIT’s Machine Learning for Healthcare course examines the role of machine learning in healthcare through a systematic exploration that begins with the nature of clinical data. The course enables students to understand, both theoretically and practically, key application areas such as risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and the improvement of clinical workflows.

While teaching students how to work with high-dimensional and irregular datasets such as electronic health records, the course also addresses ethical and technical challenges including accuracy, fairness, and generalizability. This comprehensive perspective allows students to evaluate machine learning algorithms not merely as technical tools, but as essential components of clinical decision support systems.

By the end of the course, students gain hands-on experience in health data analysis and develop knowledge and skills in critical areas such as disease prediction, personalized treatment recommendations, and the design of clinical decision support systems.

Instructors

Prof. Peter Szolovits
A distinguished academic at MIT who conducts pioneering research at the intersection of medicine and computer science.

Prof. David Sontag
A leading figure in health AI, specializing in the development of algorithms that enable meaningful insights from clinical data.