Welcome to the class Computation Perception Extended - F26
Format: Weekly sessions + guided self-study + final project
Audience: Students in art, design, and creative technology
Prerequisites: No advanced ML background required (basic coding literacy is recommended)
Short class description:
AI tools are everywhere, but understanding how they work opens very different creative possibilities.
This module explores the open-source roots of ML/AI, from neural-network foundations to current tools and practices.
Through hands-on experimentation with open-source frameworks, we will move beyond proprietary interfaces and develop independent creative workflows.The class ends with a final project with transparent process, documentation, and code.
- Students integrate AI models into experimental and artistic contexts to develop original digital experiences.
- Students adapt and remix existing AI systems or datasets to create new forms of expression.
- Students analyze and evaluate how AI influences meaning, participation, and authorship in digital art and design.
- Students translate technical exploration into coherent and meaningful creative outcomes.
- Students demonstrate transparency in design and decision-making when working with AI systems and data.
- Students evaluate the balance between automation and creative agency within AI-driven practices.
- Students reflect critically on the cultural, ethical, and social implications of AI in art and design.
The class is composed from independent block repositories added in content/blocks/ as git submodules.
| Week | Block | Topic | Recording |
|---|---|---|---|
| 01 | block-ai-intro-core |
AI/ML/DL foundations, history, and key concepts | 🎞️ |
| 02 | block-ai-image-gen |
AI/ML/DL image generation | 🎞️ |
| 03 | block-ai-text-generation |
AI/ML/DL text generation | 🎞️ |
| 04 | --- |
Getting started with Replicate | |
| 05 | block-ai-audio-generation |
AI/ML/DL audio generation |
Notes:
- Blocks are maintained in their own
block-*repositories. - Slides are maintained in separate
slides-*repositories.
- Participation, attendance, and engagement: 33%
- Research and documentation: 33%
- Final project: 33%
Provide detailed grading rules in syllabus.md.
This course is structured as an active studio environment.
Each session begins with a short collective perception round, and may include participatory formats depending on the current block.
The detailed description of these formats is available here:
Engagement is a core part of the learning process in this class.
- Python + notebooks (Jupyter / Colab)
- Open-source ML/AI libraries and models
- Git-based documentation and project sharing
Use external code, datasets, and references responsibly:
- Cite all reused code and resources.
- Clearly distinguish your own work from borrowed material.
- Acknowledge all collaborators and support received.