Visual Culture through the Computer's Eye

Catalog description

Visual studies and the humanities more generally have thought about and modeled seeing of artworks for many centuries. What useful tools can machine learning develop from databases of art historical images or other datasets of visual culture? Can tools from machine learning help visual studies ask new questions? When put together, what can these fields teach us about visual learning, its pathways, its underlying assumptions, and the effects of its archives/datasets? Class project teams will ideally be composed of both humanities majors and engineering majors who will develop datasets and/or ask important questions of datasets, in addition to thinking and writing more generally about how computer vision could help in teaching and analyzing visual art. We are looking for a variety of students from different majors and schools to bring their diverse skill sets to the course. No programming knowledge is required. The course offers an example-based introduction to machine learning, so no prior knowledge of machine learning is required.