Data and Machine Learning for Creative Practice
Budget: $30 – $250 USD
Explore one of the module topics (e.g. facial recognition, pix2pix, stylegan, posenet,
style-transfer, charrnn, sound classification, neural networks) in an individual final
project. Your project should be well informed, demonstrating your understanding of
the technology as well as your ability to connect this with your creative practice.
Although you should include at minimum one of the module topics, you can include
others and expand upon our exploration of the topic.
This project is worth 60% of your overall module mark. In line with the Computing
undergraduate programme specifications, this “extended practical coursework”
should take roughly 50 hours of work (assuming you have kept pace with the module
throughout the term).
This project should demonstrate mastery of at least a subset of the module learni
style-transfer, charrnn, sound classification, neural networks) in an individual final
project. Your project should be well informed, demonstrating your understanding of
the technology as well as your ability to connect this with your creative practice.
Although you should include at minimum one of the module topics, you can include
others and expand upon our exploration of the topic.
This project is worth 60% of your overall module mark. In line with the Computing
undergraduate programme specifications, this “extended practical coursework”
should take roughly 50 hours of work (assuming you have kept pace with the module
throughout the term).
This project should demonstrate mastery of at least a subset of the module learni