This course will give a broad overview of machine learning for health. We begin with an overview of what makes healthcare unique, and then explore machine learning methods for clinical and healthcare applications through recent papers. We discuss the recent successes of graphical models, deep learning, time series analysis, and transfer learning in the context of health. We also broadly cover concepts of learning, algorithmic fairness, interpretability, and causality. We emphasize the importance of collaboration between technical and non-technical researchers, and consider the implications of machine learning in healthcare governance and policy. Students will choose and complete a course project, and make project presentations at the end of the course. This course requires a strong background in linear algebra and probability theory, or strong grades in the machine learning course. Familiarity with programming and software engineering is beneficial, but not required.