Currents: Physicality of ML
Fall 2022
Taught By: Shirley Leung
Section: A
CRN: 4333
Credits: 3
The Physicality of Machine Learning focuses on utilizing the systematic study of algorithms and turning them into a functioning model that can be implemented and actualized in the “real” – physical world. This course will be focusing on introducing students to different types of machine learning models, learning various algorithms and models that can uncover complex patterns, utilizing ready-made software, and learning how to implement them into a physical production method. Students will work in a variety of media depending on their own backgrounds and interests. In this course, students will learn how to interact with text, images, and 3D-based machine learning models and subsequently bring these created work into the physical world.
College: Parsons School of Design (PS)
Department: Art, Media & Technology (PSAM)
Campus: New York City (GV)
Course Format: Studio (S)
Modality: In-Person
Max Enrollment: 18
Add/Drop Deadline: September 12, 2022 (Monday)
Online Withdrawal Deadline: November 20, 2022 (Sunday)
Seats Available: Yes
Status: Waitlist*
* Status information is updated every few minutes. The status of this course may have changed since the last update. Open seats may have restrictions that will prevent some students from registering. Updated: 6:38pm 7/4/2022 EDT