CS-E7770 - AI for Media, Art, and Design D, Lecture, 10.1.2024-16.2.2024

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Please note! Course description is confirmed for two academic years, 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.

LEARNING OUTCOMES

On successful completion of this course, students will be able to:

  • Understand how common AI and machine learning algorithms & tools work on an intuitive, non-mathematical level, based on visualizations and practical experiments.
  • Understand what the tools can be used for in context of art, media, and design.
  • Process and/or generate images, audio, and/or text using AI and machine learning tools such as Colab Python Notebooks and Unity Machine Learning Agents
  • Continue learning and experimentation independently

For more details, see the full syllabus, code examples, and exercises at https://github.com/PerttuHamalainen/MediaAI/tree/master/Lessons (updated continuously), and follow the course Twitter feed: https://twitter.com/aaltomediaai.

Credits: 3

Schedule: 10.01.2024 - 16.02.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Perttu Hämäläinen

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

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    This is a hands-on, project-based crash course on deep learning and other AI techniques for people with only few technical prerequisites:

    • Basic programming skills (e.g., some python, processing, or Unity C#)
    • Basic high-school math. We focus on visual explanations that are enough to understand why and how various AI methods work.

    The focus is on media processing and games, which makes this particularly suitable for artists and designers.

    The lessons and materials comprise three parts:

    • Lectures
    • Software examples
    • Exercises that require you to modify the sofware examples to test your learning. The exercises provide both easy and advanced exercises to cater for different skill levels. Model solutions are provided for the easier ones, but some of the advanced exercises are left to the students and can also work as final project topics.

    Full syllabus and materials (updated for each run of the course):
    https://github.com/PerttuHamalainen/MediaAI
    Links and resources (updated continuously):
    https://twitter.com/aaltomediaai

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Completing a final project.

Workload

  • valid for whole curriculum period:

    Lectures, programming exercises, and a final project. The final project can focus on either applying or creating some AI or machine learning approach for creative use.

DETAILS

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Spring III
    2023-2024 Spring III

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof