Data Science and Decision Making
Instructor(s): William Lowe
Abstract
This course is about decision making in theory and practice. Our theoretical framework will be classical decision theory, informed by the literature on human and machine cognitive performance and bias. Our discussion of practice will also be informed by invited speakers who are chosen to be people whose jobs require them to make decisions in a technical context, e.g., to run election campaigns, evaluate government programs, or drive sales.
Prerequisites: Statistics 1 and 2, or equivalent. Familiarity with basic probability manipulations, up to Bayes theorem. Familiarity with machine learning and prediction models will be helpful but not required.
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