Machine Learning and Sensing for Healthcare

Catalog description

Today's health infrastructure makes it challenging for individuals to equitably access even basic medical resources. In this course, we will learn how to create modern health sensing systems that reimagine the way that healthcare delivery is performed. Specifically, we will learn how to transform ubiquitous smart devices around us like smartphones, speakers, and watches, as well as emerging wearables like earables and smartglasses, into personal medical tricorders that have the ability to provide access to health testing at our fingertips. We will learn how to tap into the rich sensor data streams (e.g. acoustic, vision, IMU) from these devices and understand core techniques in applied signal processing and machine learning to intelligently transform sensor data into clinically-relevant biomarkers which can be used to screen and diagnose diseases at scale. Beyond these techniques, this course delves into the full lifecycle of system building including ideation of frugal designs, iterative prototyping, pilot data collection, visualization, and debugging. Finally, to ensure our systems will have impact in the real-world we will cover important issues of privacy-preserving techniques and regulatory pathways which are important considerations when deploying health research. The course will focus on class discussions, hands-on demonstrations, and tutorials. Students will be evaluated on their class participation, multiple mini projects, and a final team project. Course Website: https://docs.google.com/spreadsheets/d/1OoZfohCyawAkynDMAQD3k9nH6hHvJ8vueVurtuZs0vo/edit?usp=sharing