Spring 2024 - IAT 813 G100

Artificial Intelligence in Computational Art and Design (3)

Class Number: 6853

Delivery Method: In Person


  • Course Times + Location:

    Jan 8 – Apr 12, 2024: Fri, 9:30 a.m.–12:20 p.m.



Applications of computational intelligence to art and design are introduced through a set of motivating examples. Specific areas of application include knowledge representation, problem solving, rule based systems, ontologies and statistical reasoning.


This course is fundamental for anyone interested in applying artificial intelligence methods in creating new technologies for learning and creative professional work. Working through a set of motivating examples from domains such as generative design, dance simulation, social interaction, adaptive user interface design and knowledge sharing in e-learning, the course provides insights on how AI techniques can be used to address important problems in art and design. The topics are presented in a comparative manner to clearly highlight advantages and disadvantages of each method which will provide the students with ability to weight benefits of a particular approach when facing a concrete problem in their research area.


The purpose of the course is to enable students to understand AI in the context of particular problems. Students will be able to distinguish between different epistemological approaches used within AI, and select a an approach for a particular problem considering benefits and limitatiosn of different AI approaches. The course will require students to apply a selected AI technique to their research.  It is recommended that students enrolling in this course have some prior programming experience.


  • Critical paper summaries and in-class participation 15%
  • Analysis of a complex problem from the students' research domains with identification of the points where AI techniques are applicable and justifying a selected AI method 20%
  • Research proposal for AI system in the student's research domain and developing a prototype for a small-scale example using AI technique selected 40%
  • Exam 25%


For a pass you need to get at least 50% score for each of the components above.




"Artificial Intelligence:  A Modern Approach" (2010) by Stuart Russell and Peter Norvig; 3rd Edition, Prentice Hall  ISBN: 9780132071482

Collection of research articles to be introduced during the course.


Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:


SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html