Machine Learning for Artists
New York University Abu Dhabi Spring 2024
Course Number: IM-3312
Prof. Michael Ang - Assistant Professor of Practice of Interactive Media
Location: C3-029 IM Lab (Tuesday) - C3-153 (Thursday) / Discord
Meeting Time: Tuesday 9:55AM - 12:35PM / Thursday 11:20AM - 12:35PM
Email Address: michael.ang@nyu.edu
Office Hours: By Appointment / Zoom / Discord
Credits: 4
Course website:
https://github.com/nyuad-im/artintel
Brightspace:
https://brightspace.nyu.edu/d2l/home/351324
Schedule Overview (Spreadsheet)
Weekly Schedule / Lecture Notes
Projects Spreadsheet - submit your project links here
Reading Responses (Brightspace) - sign in with NYU account for access
Discord channel #artintel: invite on Brightspace
Course Description:
Artificial intelligence and machine learning algorithms affect many aspects of our lives whether we realize it or not: banking transactions, healthcare treatments and diagnoses, entertainment recommendations, smart car functionality, customer service agents, financial trading… the list goes on and on. The power of these algorithms lies in their ability to leverage computers to "study" and "learn". Instead of programming a computer to do a specific task, we program the computer to train itself how to do any number of tasks. As artists, how can we harness the power of these algorithms and apply them towards creative endeavors? This class will explore that basic question. Through a combination of high level applied machine learning techniques, speculative design of artificial intelligence, and some basic understanding of how these algorithms work at a low level, students will explore this rich new field. With their machine counterparts, they will create images, sounds, text, intuitive interactions, chatbots, and more.
Learning Outcomes:
- Foster an ability to think critically about AI in terms of design, ethics, and global impact
- Develop a speculative artistic vision of the future that leverages both critical perspectives and creative applications of emerging AI technologies
- Create intuitive interactions using machine learning
- Understand some of the low-level math and code used in neural networks and deep learning
- Use current tools to create works using machine learning algorithms
- Use various types of neural networks to generate sound, visuals, and text