Course syllabus for Machine learning and AI through artistic innovation

The course syllabus contains changes
See changes

Course syllabus adopted 2024-11-11 by Head of Programme (or corresponding).

Overview

  • Swedish nameMaskininlärning och AI genom konstnärlig innovation
  • CodeTRA385
  • Credits7.5 Credits
  • OwnerTRACKS
  • Education cycleSecond-cycle
  • DepartmentTRACKS
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 97156
  • Minimum participants8
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0123 Project 7.5 c
Grading: TH		 3.8 c3.7 c

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)

Course specific prerequisites

General for all Tracks courses:
In addition to the general requirements to study at the first-cycle level at Chalmers, necessary subject or project specific prerequisite competences (if any) must be fulfilled. Alternatively, the student must obtain the necessary competences during the course. The examiner will formulate and check these prerequisite competences. The student will only be admitted in agreement with the examiner.

Additional specific prerequisites for the course:
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.

Both bachelor and master's students are welcome.

Aim

General for all Tracks courses: The course provides a platform to work and solve challenging cross-disciplinary authentic problems from different stakeholders in society such as the academy, industry or public institutions. Additionally, the aim is that students from different educational programs practice working efficiently in multidisciplinary development teams.

Course specific aim: The course encourages students to approach new machine learning and AI technology with an innovative and exploratory perspective through artwork development, in which the students will integrate critical discussions on the societal implications of machine learning and artificial intelligence.

Learning outcomes (after completion of the course the student should be able to)

General learning outcomes for Tracks courses:
  • critically and creatively identify and/or formulate advanced architectural or engineering problems
  • master problems with open solutions spaces which includes to be able to handle uncertainties and limited information.
  • lead and participate in the development of new products, processes and systems using a holistic approach by following a design process and/or a systematic development process.
  • work in multidisciplinary teams and collaborate in teams with different compositions
  • show insights about cultural differences and to be able to work sensitively with them.
  • show insights about and deal with the impact of architecture and/or engineering solutions in a global, economic, environment and societal context.
  • identify ethical aspects and discuss and judge their consequences in relation to the specific problem
  • orally and in writing explain and discuss information, problems, methods, design/development processes and solutions

Course specific learning outcomes:
  • Create and critically compare concept ideas where machine learning or artificial intelligence is applied in artistic contexts
  • Realize projects from a concept to a working prototype, with integrations of practical machine learning or artificial intelligence aspects and by using innovation toolkits
  • Critically analyze societal implications of an artwork where machine learning or artificial intelligence is either integrated into the artwork or used in the making of the artwork.

Content

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.

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.

Organisation

General Tracks organisation:
The course is run by a teaching team. The main part of the course is a challenge driven project. The challenge may range from being broad societal to profound research driven. The project task is solved in a group. The course is supplemented by on-demand teaching and learning of the skills necessary for the project. The project team will have one university examiner, one or a pole of university supervisors and one or a pole of external co-supervisors if applicable.

Specific organisation for the course:
The course is run by a teaching team. The course content has three main parts: lectures, hands-on technical workshops, and course projects. The lectures cover art, technology, and innovation in relation to machine learning and artificial intelligence topics. The workshops cover hands-on technical skills with integrations of practical machine learning and AI. The lectures and workshops aim to help students towards their group project. The projects are the productions of artworks either integrating machine learning and ai or using ML and AI in artwork making processes. The students work in groups to develop the course project which is presented towards the end of the course as an artwork or performance. The final submission is a post-production analysis of the course project in its societal discourse.

Literature

With input from the teaching team, students will develop the ability to identify and acquire relevant literature throughout their projects.

Examination including compulsory elements

To pass the course the following need to be fulfilled:
  • Attending and active participation to one of the creative coding workshops, one of the five prototyping workshops, two workshops on ML and AI for artistic applications,
  • Attendance to all lectures
  • An analysis report on an approved artwork in literature and institutional archives (10% of grade, individual work, graded on coherency, and integration of literature)
  • Project proposal report and its presentation to the class (group work, 20% of total grade, assessed by novelty, aesthetics, impact, and feasibility)
  • Full participation to course project
  • Production of approved project
  • Project Design iterations report and its presentation to the class (group work, 25% of total grade, assessed by integration of design literature, technology research and development, and feasibility)
  • Final presentation (attended, 15% graded on presentation aesthetics, production quality, and feasibility)
  • Final project report - analysis of societal impact (individual work, 30% of total grade, assessed by coherency, integration of literature, critical analysis)
  • 75% overall attendance to all mandatory course activities is required to pass the course
  • Bachelor and master students are graded according to separate standards.
  • The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.

The course syllabus contains changes

  • Changes to course:
    • 2024-11-11: Aim Aim changed by UOL
      Updated purpose
    • 2024-11-11: Learning outcomes Learning outcomes changed by UOL
      Updated learning outcome
    • 2024-11-11: Content Content changed by UOL
      Updated content
    • 2024-11-11: Organization Organization changed by UOL
      Updated information about organisation
    • 2024-11-11: Examination Examination changed by UOL
      Updated information about examination
    • 2024-11-11: Litterature Litterature changed by UOL
      Updated information about literature
    • 2024-11-11: Prerequisites Prerequisites changed by UOL
      Updated course specific prerequisites
  • Changes to course rounds:
    • 2024-09-12: Block Block B added by Examinator
      [Course round 1]