Analyzing Biases in the Age of Digital Data

Catalog description

Introduction to the algorithms of machine learning that are applied in the real world, considering biases, methodological limitations, and pitfalls of data. It provides students with a framework for analyzing the social, political issues, enabling them to think critically about ethical boundaries. The topics of the course draw from classical statistics, machine learning, ethics and privacy. Course overlaps with: STAT 303. Prerequisite: CSS 301; and either CSS 340 or CSS 342; recommended: experience coding non-trivial functions to manipulate datasets; and previous coursework in probability, statistics, and algorithms.