Teaching Makematics at ITP

I’m proud to announce that I’ll be teaching a class a NYU ITP next semester. The class grew out of my work on Makematics; it’s called “Makematics: Turning Computer Science Research into Creative Tools”. Here’s the full description from the Fall 2012 course listings (which will sound somewhat familiar to you if you read my intro to Makematics):

Artists build on top of science. Today’s cutting edge math and computer science research becomes tomorrow’s breakthrough creative projects.

Computer vision algorithms, machine learning techniques, and 3D topology are becoming vital prerequisites to doing daily work in creative fields from interactive art to generative graphics, data visualization, and digital fabrication. If they don’t grapple with these subjects themselves, artists are forced to wait for others to digest this new knowledge before they can work with it. Their creative options shrink to those parts of this research selected by Adobe and Autodesk for inclusion in prepackaged tools.

This class is designed to help you start seizing the results of this research for your own creative work. You’ll learn how to explore the published academic literature for techniques that will help you build your dream projects. And you’ll learn how to use those techniques to make those projects a reality.

Each week we’ll explore a technique from one of these research fields. We’ll learn to understand the original research and see how to implement it in code that you can use in your projects. You’ll learn to use the marching squares algorithm to detect fingers or make 3D models into something you can laser cut. You’ll learn how to use support vector machines to train your own object detector or analyze a body of text. We’ll cover a series of such topics, each of which has a wide range of applications in different creative media.

I’m still working on finalizing exactly the technical topics I’ll be covering. So far I have units planned on Marching Squares, Support Vector Machines, and Principle Component Analysis. I’m looking for a good topic in probability (and am open to suggestions). I’ll be teaching the class in Processing and producing libraries that facilitate each of these techniques (in fact, I’ve already started).

In addition to the motivations for this topic mentioned in the class description above, I also have another pet reason why I think this material matters. I hope this type of curriculum might be the start of something like an applied version of the New Aesthetic, teaching a set of skills and a body of knowledge that can move us beyond simply goggling at the output of drone vision systems, poetic spambots, and digitally fabricated high heels into deeply understanding the cluster of technologies that produce them and, in turn, using that understanding to produce things of our own. There’s no way a single 7-week class can hope to make more than a small start at a project like that, but a start is what comes first.

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