The workshop introduces the theory and application of Machine Learning for creative and artistic practices, experimenting with applications to music and sounds, video and images, text, as well as physical computing managing algorithms.

One week full-time program to investigate on core algorithms used for parsing, visualizing, and discovering patterns in complex multimedia data. We will learn how to use neural networks to create real-time, cross-modal interactions for use in video and installation, as well as live music performance and data journalism/activism. We will also provide tools and code for clustering, retrieving, and visualizing large collections of multimedia. Finally, we will use the Fab Lab machinery to prototype those project parts needing physical items support.

The goal of the workshop is to provide techniques and tools for rapidly building prototypes, both software-based and physical, that leverage machine learning in some way. High-level tools will be provided in advance, along with source code for modifying it.

Prior coding experience in a text-based (Python, Java, C++) or patch-based (Max/MSP) programming environment is very helpful to understand how to modify the provided software, but not necessary, although a coding literacy is warmly suggested to all interested in participating to the workshop. Applicants across many disciplines are welcome. Musicians, installation artists, hardware hackers, computer scientists, data journalists, activists and many others will find the techniques broadly applicable to their craft.