Term: Spring 2023 (Feb 1 – May 10)
Days/Times: Wednesdays, 9am-4pm
Location: MacLean 401
Contact me: email or Canvas message
Computer Vision is the scientific field that deals with programming computers to “see” the world. From its inception as a research discipline in the early 1960s, the goal of computer vision was to accelerate the development of artificial intelligence, the idea being that in order for a computer to act on the world, it must first understand it–making a computer “see”, it was hypothesized, was the best way to teach a computer to “know.”
In this course, we engage the field of computer vision as both a fertile territory for artistic exploration and possibility, and as a complex subject for critical interrogation. Through technical demonstrations and project-based experimentation this course will introduce a variety of tools for creating interactive art and machine learning-based generative imagery. Throughout the semester, we will confront the ethical implications of computer vision technologies both as artists building with these tools, and as citizens in a world where these technologies are ubiquitous, powerful, and often harmful.
This course will teach both “traditional” and machine learning-based approaches to computer vision. In the first part of the semester, we will focus more on writing code (using p5.js) to manipulate images, video and live webcam feeds at the pixel level. In second, we will shift to a data-first approach, writing code (in Python) to help us create data sets used to train generative neural networks.
By the end of this course, you should have the capability to continue exploring computer vision tools independently, as well as the critical understanding to work with these tools responsibly and effectively.
Douglas Rosman (he/him/his) (call me Doug)
[email protected] | dougrosman.com
Yuwen Huang (She/her) (Yvonne)
[email protected] | yuwenhuang.net
Send me an email ([email protected]) or Canvas message. I will be slow to respond on weekends.
(Occasionally) Thursdays 3:30pm - 4:30pm in the Flex Space (MC 400). Part-time faculty like myself are not required to hold office hours. I will give you all advance notice if I will be holding office hours.