Machine Learning for Artists – Video lectures and notes

It's no secret that machine learning - more specifically, deep learning - has been playing an increasingly noticeable role in the world of art, as of late. From Deep Dream, to Deep Forger, to Beyond the Fence, and further, all varieties of art have been touched by the creativity of neural networks, and it seems that this has not gone unnoticed by those outside of the direct sphere of machine learning.

Gene Kogan, of the Tisch School of the Arts at NYU, has recently started up his inaugural offering of Machine Learning for Artists, an elective course in the school's Interactive Telecommunications Program (ITP). The ITP has the mission of exploring "the imaginative use of communications technologies," and how they may be leveraged for bringing art and delight into the lives of individuals. They self-identify as "a Center for the Recently Possible," a term I think is fantastic.

The course is not overly-technical, and focuses on transferable machine and deep learning understanding which would be both transferable and reusable. Directly from the program's course description website:

This course will not cover, nor assumes knowledge of, the technical or mathematical details of machine learning, instead focusing on how to integrate available tools into existing interactive applications, although resources for learning the technical aspects will be provided. The tools we use will be platform-agnostic, making it easier to add machine learning into existing applications. Programming ability will be helpful in customizing the provided tools, but is not required.

The best part of this course (for those of us not in a position to move to NYC and enroll in a 2 year graduate program), the course materials are freely-available here. At the time of writing, the following lectures have been posted (direct links):

• Introduction, neural networks
• Applications, Wekinator
• Convolutional neural networks
• Convnet applications
• Recurrent neural networks

The class really seems to focus on what can be done with machine learning, vis-a-vis creating, and bettering, art. The most recent addition of a recurrent neural networks lecture suggests that this course will not be solely focused on the visual arts, as it makes a foray into text-generation and character-level models.

Kogan is also writing a book on the subject. You can check out draft chapters of Machine Learning for Artists here, and view the figures and interactive demos as they become available here.

We congratulate fine institutions such as NYU for their recognition of the importance of making their quality course material freely and openly available to the public; we likewise tip our hats to Gene Kogan for his recognition and interest in doing the same.

You can find out more about the Tisch School of Arts ITP program here.

Related:

• Deep Learning and Artistic Style – Can art be quantified?
• Top /r/MachineLearning Posts, August: Deep Learning paints in style of many famous painters
• Top 5 Deep Learning Resources, January