Data Encoding Seminar 

MaCAD Digital Tools for DATA ENCODING SEMINAR

Machine learning algorithms offer an alternative modelling paradigm for complex problems. Differently from analytical models where processes are computed using iterative time-based heavy calculations and pre-assigned property parameters, machine learning models learn by example through searching for an approximated relationship between the inputs and the targets. Once trained, they can be easily deployed within a design-to-fabrication workflow to offer predictions. While these models promise advantages and productivity, they are notoriously data-hungry. Many open source datasets are available online for training of state-of-the-art models, however these datasets are not relevant for architectural applications.

Source: Hesham Shawky & Cami Quinteros –  Imaginary Vessels.

Dataset design is the enabler to using Machine Learning algorithms in different fields. While an existing ecology of algorithms provides architectures suitable to specific types of problems be classification or regression, the models must be trained on datasets that are specific to the problem they are expected to solve. They can be compiled through web-scraping, or be generated, either computationally through heuristic algorithms, or through sensing and digitising physical samples. Dataset design requires several criteria to be followed: Increased sample diversity, bias avoidance and ambiguity avoidance. These criteria ensure that the problem is well represented, with equal distribution and without confusion.

In this seminar, you will be introduced to conceptual perspectives around machine learning, dataset design and feature encoding. Through direct hands-on experience, you will explore the usage of machine learning for your project. You will be tasked to develop a computational workflow to produce a dataset and train an adequate model for your predictive task. Through the study of the parameter space and feature distribution and representation, you will propose a dataset encoding method, and evaluate it through the training of shallow models as well as artificial neural networks, and represent the predicted outcome in the design space. This is an occasion to step back from “mega-models” such as LLMS and Diffusion Models, and build a foundational understanding of the basics of ML. 

Learning Objectives 

At course completion the student will:

•   Become knowledgeable of fundamental machine learning concepts and workflows.
•   Understand notions of parameter space, data encoding and feature selection
•   Acquire competences in developing custom datasets for architectural application
•   Acquire competences in feature engineering (dimensionality reduction, data analysis)
•   Acquire a hands-on experience in using state of the art machine learning libraries
•   Develop appropriate representational methods and tools to showcase your findings
•   Collaborate effectively within a group-working exercise