IMK153 Artgorithms: Algorithmic Art – practice

Faculty of Arts
Spring 2021
Extent and Intensity
0/2/0. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Tomáš Staudek, Ph.D. (lecturer)
Guaranteed by
Mgr. Tomáš Staudek, Ph.D.
Department of Musicology – Faculty of Arts
Contact Person: Bc. Jitka Leflíková
Supplier department: Department of Musicology – Faculty of Arts
Timetable
Fri 5. 3. 9:00–10:40 N31, Fri 9. 4. 9:00–10:40 N31, Fri 7. 5. 9:00–10:40 N31, Fri 28. 5. 9:00–10:40 N31
Prerequisites
The course is devoted to intersections of art, math and algorithms. It deals with practical applications of software aesthetics and programmable imagery in visual arts. Enrolling the course assumes artistic sensitivity and intermediate user knowledge of image editing software.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 40 student(s).
Current registration and enrolment status: enrolled: 0/40, only registered: 0/40, only registered with preference (fields directly associated with the programme): 0/40
fields of study / plans the course is directly associated with
Course objectives
— Get acquainted with state-of-the-art and current tendencies in generative art.
— Deepen knowledge of mathematics and computer science applications in visual arts.
— Understand principles of exact aesthetics during design and criticism of artworks.
— Aquire practical skills in utilizing software tools for creating generative art.
— Participate in collective exhibition.
Learning outcomes
After accomplishing the course students will be able to
— understand algorithmic concepts and tools in art production;
— devise and implement own generative design frameworks both in fine and applied arts;
— interpret and evaluate algorithmic works of art by the means of exact aesthetics;
— cooperate in team to produce a collective art project.
Syllabus
  • 1. Color.
  • 2. Point.
  • 3. Line.
  • 4. Plane.
  • 5. Structure.
  • 6. Fractal.
  • 7. Ornament.
  • 8. Randomness.
  • 9. Optimalization.
  • 10. Evolution.
  • 11. Emergence.
  • 12. Interaction.
  • 13. Visualization.
Literature
    required literature
  • STINY, George and James GIPS. Algorithmic Aesthetics: Computer Models for Criticism & Design in the Arts. University of California, 1978. ISBN 0-520-03467-8. URL info
    recommended literature
  • CAPLAN, Craig S. The Bridges Archive. The Bridges Organization, 2013. URL info
  • FRIEDMAN, Nat and Ergun AKLEMAN. HYPERSEEING. The International Society of the Arts, Mathematics, and Architecture (ISAMA), 2012. URL info
  • MONFORT, Nick and Patsy BAUDOIN. 10 PRINT CHR$(205.5+RND(1)); : GOTO 10. The MIT Press, 2012. URL info
  • RADOVIC, Ljiljana. VisMath. Mathematical Institute SASA, Belgrade, 2014. ISSN 1821-1437. URL info
  • SCHIFFMAN, Daniel. The Nature of Code: Simulating Natural Systems with Processing. Daniel Schiffman, 2012. ISBN 0-9859308-0-2. URL info
Teaching methods
Teaching activities include seminars and standalone projects. After commented introduction to given topic a tutored practical part follows in which possibilities of artistic rendering are presented. Seminars are supported by e‑learning activities in Schoology LMS. Students are responsible for attending seminars and independent creative work guided by provided materials.

Projects are given in the form of homework assignments. For each assignment freely available applications are provided. Students‘ work will be consulted with the lecturer during seminars. Selected artworks will be displayed in the form of an exhibition.
Assessment methods
The course completed with exam. The final grade corresponds to the points earned during the semester. Students will pass the course after accomplishing half of standalone assignments (50 points). For the final project (another 50 points) students shall finalize selected artworks for the exhibition and complete a written report summarizing the applied algorithms and their parameters. Extra points can be attributed for seminar activity.

Grading scale: A = 100–90, B=89–80, C=79–70, D=69–60, E=59–50, F<50 points.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
Information on the extent and intensity of the course: 80 hodin výuky/semestr.
Teacher's information
https://artgorithms.tumblr.com/
The course is also listed under the following terms Spring 2020, Spring 2022.
  • Enrolment Statistics (Spring 2021, recent)
  • Permalink: https://is.muni.cz/course/phil/spring2021/IMK153