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 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 colla
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.
CS2541 will be capped to students who have an appropriate background this semester. If you are interested in taking the course, please come to our first lecture and fill out the course application https://goo.gl/forms/DFm2SPYZTUiVrsEk2