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
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III
2023-2024 Spring III