Apply Creative Machine Learning

Explore how machine learning is revolutionising the creative industries

From photography to music, creative AI is changing the way we interact with technology.

On this course, you’ll learn the core concepts of what machine learning is, and how machine learning works. You’ll learn how to build simple classification systems that can discriminate between different types of information, and regressions systems that can map interactions onto different outputs, like sliders on a synthesiser.

You’ll explore the full extent of machine learning systems’ abilities, specifically in relation to the creative industries.

This course is aimed at people who would like to work in the creative industries and want to know how machine learning can be used in that context. The course is also valuable for students considering a master’s degree in creative computing and want to know what it would involve, or people wanting to become a machine learning engineer in general.

You might also be interested in the other courses in the Essential Creative Technologies collection from UAL Creative Computing Institute, Lancaster University and the Institute of Coding.

Completing the course will require a basic level of knowledge of modern web development including the programming languages HTML, JavaScript and CSS. You will need a laptop with a webcam to do some of the exercises in this course. Apart from that, all examples and coding activities require an internet browser, preferably Google Chrome or Mozilla’s Firefox.

• So what is machine learning anyway?
• Welcome to the course
• What is machine learning?
• Experimenting with image classifiers
• It's all about the data
• Building a camera driven instrument
• Wrapping up Week 1
• Getting comfortable with machine learning
• Introduction to Week 2 and the MIMIC platform
• Introducing supervised learning with RapidLib
• Exploring sound spaces
• Machine learning web apps made easy with Learner.js
• Wrapping up Week 2
• Deeper with classification
• Introduction to Week 3
• Classifying with K-Nearest Neighbour
• Addressing problematic uses of machine learning with art
• Turning sound into data
• Building alternative game controllers
• Wrapping up Week 3
• Putting it all together
• Introduction to Week 4
• All about neural networks
• Techniques for using regression creatively
• But is it working?
• Next steps
• Wrapping up the course