OVERVIEW
Introduces guidelines, processes, and systems for designing effective user experiences powered by machine learning models. Topics include design tradeoffs unique to data-driven products and services such as automation versus control, precision versus recall, and personalization versus privacy. Readings from human computer-interaction, product design, cognitive science, machine learning, computer vision, and natural language processing frame in-class design exercises. Students work in teams on a multi-week research project creating or auditing data-driven experiences.
LECTURES
To support a hybrid format, class will always be delivered online synchronously via
Zoom, and the recordings for each class will be made available on this website. In addition, classes will generally be held in person in Urbana-Champaign on Tuesdays and in Chicago on Thursdays. Please refer to the syllabus for the location of each class.
We will use
Slack and
FigJam for all in-class collaborative design exercises.