Programme Schedule 2024/25

The MTD (AI Empowered Built Environment) is a one-year full-time coursework-based Master programme. It comprises eight courses (96 credits): two core design courses and six specialised courses organised as follows:

Term 1 (Sep - Dec)
Course Title Credit Points Course Type
Innovation by Design 12 Design Core
AI Empowered Data-Driven Design 12 Specialisation Core
Creative Machine Learning 12 Specialisation Core

Term 2 (Jan - Apr)
Course Title Credit Points Course Type
Design Science 12 Design Core
Smart Sustainable Development 12 Specialisation Core
Optimisation for Industry Applications 12 Specialisation Core

Term 3 (May - Aug)
Course Title Credit Points Course Type
AI Augmented Design Studio 21 Specialisation Core
Sustainable Design and Development 3 Specialisation Core

Course Descriptions

Innovation by Design (Term 1)

The focus of this course is the integration of marketing, design, engineering and manufacturing functions in creating and developing a new product, system or service. The course will go through the different phases of designing a new product, system or service using the four Ds of the four-phase Design Innovation Cycle of "Discover-Define-Develop-Deliver". The course will focus on some of the critical success factors for new product development, with an early emphasis on design thinking. Students will be given a design challenge to complete.

AI Empowered Data-Driven Design (Term 1)

This course will introduce data-driven design and planning across various scales of the built environment - from buildings to neighbourhood, town, and city. Some of the questions that will be answered in this course are: What is the role of data in design and planning in the age of smart and responsive cities? Which data is relevant? How can we derive evidence from data in order to support design, planning, and policy decisions?

The course will include lecture and hands-on components to investigate how big and small, qualitative and quantitative data, and their combination can support design and planning through the application of artificial intelligence.

Creative Machine Learning (Term 1)

The course provides an overview of today’s machine learning apparatus for generative design and speculates the ways in which architectural design process itself might be altered as a result of this epistemological shift towards a ‘Software 2.0’ paradigm. By situating the discourse within an experimental prototyping context, students will not only gain the practical experience of applied machine learning workflow, but more importantly, the architectural sensibility to conceptualise, articulate and implement their design applications in relation to these state-of-the-art artificial intelligence (AI) tools. Students are expected to work in small groups, curating and preparing the dataset; selecting and training the machine learning model; and finally generating designs from the learnt data distribution.

Design Science (Term 2)

This course introduces students to design science where many design principles and methods will be reviewed, applied and analysed. Students will learn to make connections between design science and other fields, such as engineering, and how principles in design science can be used to advance these fields. The class will cover a broad set of design methods such as customer needs analysis, methods in creativity, functional modelling, design for X and design for testing and verification.

Smart Sustainable Development (Term 2)

This course provides an interdisciplinary overview of the methods to design, simulate and track sustainable urban development in the context of global climate change. The course will investigate contemporary methods of design evaluation and improvement for sustainable development. It introduces key concepts of sustainable development, with emphasis on Net Zero Carbon, Urban Metabolism, Circular Economy and Urban Heat Island Effect.

Students will apply critical thinking skills to develop a case study report on a building or urban design of their choice. During the sessions, students will creatively apply digital simulation, scenario building and lifecycle assessment tools to the case study which is developed over the duration of the course.

Optimisation for Industry Applications (Term 2)

The emphasis of this course is applied computational optimisation for improving building performance characteristics, such as optimisation of spatial arrangement against functional requirements, simplification of complex geometry for manufacturing and assembly, minimisation of resources such as material use, labour-time, ecological impact, and costs, etc. This course is project-based, and students will learn-and-apply new concepts as they develop their solutions. It requires basic understanding and familiarity with programming language such as Python, which can be integrated within most common AEC software applications.

AI Augmented Design Studio (Term 3)

This design studio will act as a final project for the MTD (AI Empowered Built Environment) programme. The studio / project will involve industry participants where the students will have the opportunity to learn from and interact with.

Sustainable Design and Development (Term 3)

This seminar will be run in a lecture and round table discussion format. Industry and thought leaders in the Humanities, Arts and Social Science (HASS), Banking and Finance, governmental and non-governmental organisations, as well as various branches of the built environment industry, will be invited to share insights in their fields of expertise and have a deep discussion with the students.

Contact Us

For enquiries, please email: MTD@sutd.edu.sg

For applicants residing in China, please email: MTD_CN@sutd.edu.sg

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