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Abstract
This elective course presents machine-learning methods for data-driven design exploration in architectural design, and how to leverage them in combination with the parametric modelling paradigm.
Objective
The students learn how to harness parametric models for data exploration and how to augment the design process with project-specific generative deep learning models. They acquire basic understanding of the underlying methods and can implement them in their design tasks in architecture, urban planning, engineering etc.
Content
The course will cover the following topics: data exploration (analytics and visualisations), forward and inverse design (enhancing parametric modelling with machine learning), basics of machine learning, deep learning and generative models with special focus on autoencoders. The course consists of lectures providing a theoretical background followed by hands-on practical sessions with coding exercises in Python and Grasshopper. In parallel, the students will work in small groups on a semester project, in which they apply the presented data-exploration and inverse design methods to a design task of their choice. Building upon the provided framework (in Grasshopper and Python), students will generate custom datasets and train project-specific models, and then use them for concept-phase design exploration.
Resources
Lecture Notes
Slides and other materials will be provided during the course.
Literature
Literature will be provided during the course.
General Information
- Language
- English
- Levels
- BSC
- Frequency
- Semesterly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 18
- Signup End
- 23.02.2026
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Architectural Design with Machine Learning
No course on 16.03.2026 (Seminar Week) and in the last two weeks of the semester.
|
|
2 h weekly |