Innovation is often guided by a curiosity-based exploration with new technology. Machine Learning and AI through Artistic Innovation is a hands-on and practice-based course that encourages curious exploration into ML and AI technology through artistic imagination. This is a project-based course in which students explore machine learning or artificial intelligence for producing and realizing an artwork. The artworks can be installations (static, generative, or interactive) or live performances.
General information
The course activities are grouped into three parts: lectures, hands-on workshops, and a final project. The course lectures cover introduction to arts and technology; practical introduction to the fields of ML and AI; toolkits and methods for innovation, project development and management, teamwork; and methodology for investigating the societal impact of technology. The workshops are on prototyping with support from generative AI; introduction to creative coding frameworks; artificial intelligence for music and multimedia; sensors and electronics with interactive machine learning; to develop students’ hands-on skills towards their final project.
The workshops are on prototyping with support from generative AI; introduction to creative coding frameworks; deep learning for sound, image, and video; and sensors and electronics with interactive machine learning to develop students’ hands-on skills towards their final project. The final project is an artwork produced and realized as a group, either installed or performed, at the final public exhibition of the course. Through artistic imagination and exploration, students discover free-thinking and develop new perspectives to engage artistic innovation through machine learning and AI.
Prerequisites
Artistic applications of Machine Learning (ML) and Artificial Intelligence (AI) span a broad range of topics and skill sets. The students are expected to have basic knowledge in any of the emerging technologies such as creative coding or coding in general, machine learning and AI, prototyping and design, electronics and/or robotics. We are expecting students with a curiosity towards new, upcoming, and emerging technology, where hands-on exploration guides innovation.
How to apply
Apply to all Tracks courses at antagning.se / universityadmissions.se
Search for the course code TRA385.
To assess your application, please provide us with a short motivation letter (max. 1 page). The letter of motivation requested is an opportunity for each student to express specific interests and experience that cannot be seen from transcripts only. The purpose of the motivation letter is to assist the teachers in grouping students with respect to background and interests. In addition, it is possible to upload a CV that may provide additional background experience that may be relevant.
Upload the letter of motivation on your account at antagning.se / universityadmissions.se.
Alumni, PhD students and professionals apply by email to tatar@chalmers.se. Please include a letter explaining your background and how it relates to the following areas above.
More information on how to select courses
Details
Teachers: Kıvanç Tatar (examiner), Kelsey Cotton (co-teacher), Xuechen Liu (co-teacher)
Course dates: Study period 3
Credits: 7.5
Level: Advanced
Course code: TRA385