6.S897 Machine Learning for Healthcare (also HST.S53)


Graduate Level
Units: 2-0-4
Prerequisites: 6.036/6.862, 6.867, 9.520/6.860, 6.806/6.864, 6.438, or 6.034; or Co-reqs 6.036 or 6.862
Instructor:  Professor David Sontag (dsontag@csail.mit.edu)
Schedule:  W2:30-4, room 56-154
This subjects counts as an AAGS subject.
Explores machine learning methods for clinical and healthcare applications. Covers concepts of algorithmic fairness, interpretability, and causality. Discusses application of time-series analysis, graphical models, deep learning and transfer learning methods to solving problems in healthcare. Considers how newly emerging machine learning techniques will shape healthcare policy and personalized medicine.