We aim to teach the theory and practice of applying machine learning techniques to healthcare applications. Students will learn the necessary medical and biological theory for understanding the problems considered in the course, as well as the relevant algorithms and models which are used to solve them. We will focus on non-trivial situations in which applying machine learning is not only effective but also provides insight into both the medical issue and the applicable machine learning techniques. The students will be prepared to analyze healthcare tasks in a machine learning context and to select, adapt, and implement appropriate machine learning approaches. Most importantly, students will have the necessary context to navigate the complex interactions between ML and healthcare tasks.
Attendance is mandatory and will be taken via Zoom. We will lecture for 75 minutes each week to teach medical theory, overview available models, and explain homework assignments. A further 45 minutes after each lecture will be dedicated to office hours for conceptual questions and help with homework assignments. Depending on interest, we may hold office hours at other points in the week. Throughout the course, there will be short readings and coding assignments. These assignments will primarily consist of solving a classification or regression problem in the domain of medical science using machine learning techniques. We will provide the datasets and compute necessary, along with skeleton code.
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|Machine Learning in Healthcare||Max Smolin, Alex Nails, Arjun Sripathy, Athena Leong, Ashwin Reddy, Osher Lerner||50||Online||[W] 7:30PM-9:30PM||09/16/2020||Open||98||198|