Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


Students will learn overall models of networks behind deep learning and Artificial Intelligence, start building AI with their familiar computational tools. The course will focus on computational tools and applications with only minimal mathematical formalism. The course will provide application code templates built as packages in Python and in TensorFlow, interfaced more fluidly with the common computational platforms that arts, design and game students are familiar with such as Pure Data, Unity. Students will use these templates to work on the course exercises as well as to build their artistic, design and game projects.

Students will learn the foundations of deep learning and procedural artistic and design content generation, understand how to build an AI, make a case study to determine how AI functions in arts, design and games; utilising computational tools that are self-aware, perceive their own states and the state of the surrounding environment and are able to make decisions related to content generation processes.

By the end of the course, students will know powerful ways to use advanced computational methods to manipulate data and to deploy advanced data tools for interaction ( arts, design, game) content analysis and synthesis. Students will be skilled enough to build AI adaptable to any arts, design game environment in real life.

This is a project based course, students will be learning by doing, working on specific projects, receiving instant and customised feedback on their work. References to scientific papers will be provided for those who want to go deeper into the math.

Credits: 3

Schedule: 16.04.2019 - 03.05.2019

Teacher in charge (valid 01.08.2020-31.07.2022): Perttu Hämäläinen, Koray Tahiroglu

Teacher in charge (applies in this implementation): Perttu Hämäläinen, Koray Tahiroglu

Contact information for the course (applies in this implementation):

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):



  • Valid 01.08.2020-31.07.2022:

    AI and deep learning soon will become general purpose computation for arts, design and game content generation, for building anything as these computational features became more accessible and modular, allowing us to interface with any new media content. They are open source, free to use.

    What is lacking is an accessible course that empowers artists and designers to utilize the techniques, e.g., to boost their innovation capabilities through mixed-initiative co-creation (AI & ML as idea/design generators), and to rapidly evaluate and test their designs using AI agents. The new MA course Intelligent Computational Media will provide advanced practical and theoretical content regarding both generative and discriminative algorithms applied to various forms of media. Examples include but are not limited to algorithmic generation of video game content, computational music, sound installations, automatic testing and balancing of games, and intelligent image and 3D content editing. The course will utilise interactive visualizations / explorable explanations, and practical exercises & examples with source code, based on machine learning frameworks such as Tensorflow and PyTorch.

Assessment Methods and Criteria
  • Valid 01.08.2020-31.07.2022:

    The main objective of the course Intelligent Computational Media is to introduce advanced computational tools and give an opportunity to students to learn and develop their skills in deep learning, procedural artistic and design content generation with AI methods. Equally important part of the course is that we dedicate half of the contact hours for project work in the classroom where the lecturer supports students by giving sufficient guidance, feedback and tutoring. The students submit their project ideas / proposals and during this course they develop; their problem solving abilities, design decisions, depth of understanding through the challenges they face in computational environment, aesthetic and originality of their projects’ synthesis and analysis implementation, user interface design strategies in relation to the production of the project, code design quality in terms of the ways they use deep learning and AI understanding to come up and develop alternative solutions for their idea generation and project implementation. At the end of the course, students present their projects and they receive feedback / comments both from the lecturer and students. Each student project work is assessed with the criteria on the above-mentioned objectives of the course

  • Valid 01.08.2020-31.07.2022:

    exercises, assignments, this is a project-based course; at the end of the course students will submit and present their group or individual projects.


  • Valid 01.08.2020-31.07.2022:

    DOM-E5080 Game Design, DOM-E5092 Software Studies for Game Designers(V) (3-5 cr), (DOM-E5074) Composing With Data Flow Programming or equivalent expertise. Knowledge of linear algebra, probability, and statistics helps but is not mandatory; suggested courses specifically for sound students (DOM-E5067) Sound and Music Interaction and (DOM-E5043) Physical Interaction Design



Registration and further information