Introduction to Machine Learning for Designers
This course will provide an introduction to the rapidly advancing area of research in Artificial Intelligence and Machine Learning. Designers will come away with a computational toolkit enabling them to leverage AI in their projects and focus areas.
The course will cover the subfields of unsupervised machine learning for generative design, providing state of the art techniques for visual representation and exploration, reinforcement learning for modelling behavior design, and multi-agent learning for modelling interaction between agents. For designers, the advances in these techniques pose rich application areas ranging from image processing to urban design.
Access to these techniques has been democratized through easy to use, high level Application Programming Interfaces (APIs), providing designers and artists who may not have an extensive computational background with new tools to enhance and reimagine their work. Students will learn the basics of neural networks and deep learning. We will cover foundational ideas as well as core skills in ML frameworks such as Keras, Tensorflow, Pytorch etc. Students will complete the course with a hands-on design project.
As students gain expertise in developing AI models, they will also learn to weave in critical perspectives through which to interrogate core issues such as bias in AI models and its implications in the near future. Prerequisite is basic coding. No prior knowledge of machine learning is assumed. Python experience welcome but not required.
Up to five seats will be held for MDes students.
This course will be taught online through Friday, February 4th.