Computational Intelligence for Integrated Design

Computational Intelligence encompasses theory and application of computational methods, techniques and tools that have the ability to learn based on given datasets, models and tasks. It includes AI comprising machine learning, bringing together concepts from probability and statistics to programming and optimisation. It is increasingly applied in the building sector, both to help understand the current status of built environment and to make informed (design) decisions based on predicted future responses. It mines data and translates them into actionable information. It harnesses and helps understanding information to turn it into applicable knowledge. This course will focus especially on the potential of Computational Intelligence for Integral Design in architecture and engineering, intended as a process of integration across disciplines.

In this course you will learn about the current state-of-the-art of Computational Intelligence applied to architectural design and engineering, and about the theory and fundamental knowledge required to understand how to critically use (and eventually develop) your own Computational Intelligence tools. Topics of optimisation, probabilistic analysis, and machine learning will be covered, from distribution fitting and sampling, to regression, neural networks, and evolutionary algorithms, among others. You will also experience a design process where you will apply such techniques to a small-scale project, developing your design process with Computational Intelligence methods and tools.

This course uses two types of assessment: writing assignments and design examination. Specifically, your work will be assessed by reviewing the following end products:

• A short essay on critical positioning and identified opportunities based on the state-of-the-art;

• A critical reflection on workshops content, process and outputs;

• A presentation and report on the process and results of the design-related project.

Students should achieve a minimum grade of 6.0 to pass this course. In case a grade is 5.0 or 5.5, students will get the opportunity to repair the assignment within a period of 2 weeks from the result announcement, a successful repair will be assessed with a 6.0.

(A retake can be done in the next year)