We discuss the recent successes of of graphical models, deep learning, time-series analysis, and transfer learning in the context of health.
Students will choose and complete a course project, and make project presentations at the end of the course.
We require that students have the appropriate background based on several criteria to foster an interesting and interactive course. We are particularly interested in students with a passionate interest in pursuing a research project related to natural language, and also students prepared for the challenging programming aspects 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.