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ART185AI: Machine Learning and the Arts

Tuesdays and Thursdays, 9-10.50, Arts 1344. Office hours TBD.



This class provides a high-level introduction to machine learning for art and art history. Many of the tasks that machine learning models are facing today are aesthetic tasks, ranging from the classification of images (CNNs) to the generation of completely new images (GANs). At the same time, the technical opacity of machine learning models makes it difficult to properly evaluate what exactly is being learned to solve these tasks. Hence, the once speculative phenomenological question "how does the machine perceive the world" has become a real-world problem. We will approach this problem by means of technical and critical close readings of contemporary machine learning algorithms. The first part of the class will explore the history and philosophy of computer-generated images, while the second part of the class will be dedicated to studying and replicating concrete machine learning results, mostly from the domain of image synthesis. At the end of the class, participants will be able to evaluate future developments in machine learning for art and art history, as well as potentially integrate machine learning approaches into their own practice.


In-class participation: 25%, assignments: 25%, final project/paper: 50 %.


Syllabus I: Theory

1/8: Introduction

1/10: The Current State of Machine Learning and the Arts

Weekly assignment: Find an artwork that has been made using computers. Briefly (300 words max.) describe the artwork and the role the computer has played in its production. Send an image of the work and the description to me until next Monday at noon. We will review some of the works in class on Tuesday.

1/15: History of Computer Art I

  • 📖 Nees, Georg, Computer-grafik (1965, German, translation here, but browse the images in the original book)
  • 📖 Klütsch, Christoph, Computer Graphic-Aesthetic Experiments Between Two Cultures (2007)
  • 📖 Higgins, Dick, Computers for the Arts (1968)
  • 📼 Manfred Mohr video works playlist

1/17: History of Computer Art II

Part of P-036, "White Noise" by Manfred Mohr (1970)

  • 📖 Klütsch, Christoph, The Summer 1968 in London and Zagreb: Starting or End Point for Computer Art? (2005)
  • 📖 Cybernetic Serendipity: The Computer and the Arts (1969, 2018 reprint)
  • 📖 Software. Information Technology: Its New Meaning for Art (1970)
  • 📖 New Tendencies 4: Computers and Visual Research (1970)

Weekly assignment: Carefully prepare the readings for next week. Research unfamiliar concepts and note questions. Brief quiz on "Computing Machinery and Intelligence" next week.

1/22: Turing Test and Chinese Room

1/24: Neural Networks

Syllabus II: Practice

1/29: Hands-on Python I

1/31: Hands-on Python II

Weekly assignment (not graded): Finish the in-class exercies and push to the lab repository.

2/7: Hands-on Python III

Weekly assignment (not graded): Finish the in-class exercies and push to the lab repository.

2/12: Hands-on Python IV

2/14: Hands-on Python V

Weekly assignment: First final project proposal abstract (300 words), due Thursday next week.

Syllabus III: Theory and Practice

2/19: Language Models

2/21: Deep Dream

Deepdream experiment by Kyle McDonald (2015)

Weekly assignment: Create your own (interesting) deep dream image(s), due Tuesday next week.

2/26: Feature Visualization

2/28 Bias and Interpretability

Weekly assignment: Final project proposal (1-2 pages) due next week. Sections to include: project description, technical component, critical component, feasability.

3/5: No class

  • Fabian at Humboldt University, Berlin. Please use the time to work on your final projects.

3/7: GANs

Barbershops genrated with the BigGAN algorithm (2018)

3/12: Final projects

  • Final project studio

3/14: Final projects

  • Final project presentation

Further Resources

For a list of further resources, see:

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