About this online course


Learn about the elements of AI, machine learning and computer vision as applied to architectural design practice and research.

Are you navigating through the maze of AI discussions in everyday conversations? Do you feel overwhelmed and find it challenging to keep up with the constant flow of AI news? Or perhaps you are enthusiastic about AI and its transformative power in design practices. This course will shed light on the science behind the most popular AI tools. 

Are you an architect concerned about the potential impact of AI on your role? If you're eager to upskill, this course is designed to help you manage expectations and enhance your skills, ensuring greater job competency in the evolving landscape of design, data and AI.  

The course goes beyond introducing AI as merely a tool but presents a new methodology for scientific design thinking, focusing on a few key elements to empower your designs with Artificial Intelligence.

The content of the course is specifically suitable for architects in practice or architectural students searching for something outside of the architecture field, possibly gaining new skills in programming and AI to fit into more diverse job opportunities.

The learning journey starts with understanding machine learning as the science behind the AI technology. Further, the focus is established on computer vision as the “eye of AI” within the domain of architectural design. You will discover how the computer vision technology reshapes the landscape of design possibilities and merges creativity and technology. 

You will also be introduced to algorithmic and data-driven thinking, data patterns, and the transformative power of learning systems. Hands-on experience with Python programming is included in the course. The assessments will include a brief machine learning project that combines theory with real-world application.

Both scientific and computational approaches are presented in the course. You will learn how to formulate hypotheses and explore innovative ways of testing and validating your design concepts. By exploring statistical machine learning for design validation, you will be able to translate your design hypotheses into reality by employing intuitive statistical machine learning methods, refining your designs through empirical validation and at the same time acquiring the skills to make informed design decisions. 

What You'll Learn

  • Explain machine learning as a science behind AI technology. 
  • Describe what computer vision is and how it is positioned with respect to AI technology. 
  • Recognise some applications of computer vision in architectural design. 
  • Learn how and where to find data related to the built environment. 
  • Learn how to re-think design as a scientific quest.
  • Gain hands-on experience of Python programming and using relevant libraries to conduct a small machine learning project with real data. 


Course Syllabus

Module 1: Understanding AI 

  • Data, information, knowledge 
  • AI, machine learning and computer vision
  • Deep learning frameworks, Supervised learning, Clustering and Unsupervised learning, Reinforcement Learning, Dimensionality  Reduction, Visualization  

Module 2: Comprehension - Machine learning for design problems

  • Algorithmic thinking vs Data driven thinking.
  • Validating architectural quality with data 

Module 3: Application  - The design question 

  • Apply AI knowledge to re-formulate a design question 
  • Defining real-world problems with different approaches (algorithmic, data driven, machine learning)

Module 4: Analysis - Python programming for the design question 

  • Learn how to use Python programming and relevant libraries to collect and curate data to approach the formulated design question. 
  • Build up practical skills to approach the data-driven design questions.


This is a Massive Open Online Course (MOOC) that runs on EdX.

The course is an introductory course to AI, so there are no prerequisites.

  • Starts: Sep 24, 2024
  • Free | Earn certificate for $149
  • Group fee: contact us
  • Length: 4 weeks
  • Effort: 2 - 4 hours per week

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