BS723 MACHINE LEARNING APPLICATIONS IN ARCHITECTURE
| Course Code: | 8540723 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (2.00 - 2.00) |
| ECTS Credit: | 8.0 |
| Department: | Building Science |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Prof.Dr. ARZU SORGUÇ |
| Offered Semester: | Fall and Spring Semesters. |
Course Objectives
This course aims to provide a comprehensive introduction to machine learning (ML) and its relevance in architecture and computational design. Students will develop a solid understanding of core computational concepts, including functions, parameters, variables, algorithms, and problem spaces, particularly in the context of design thinking. The course introduces foundational knowledge in probability and optimization theory, followed by the study of machine learning algorithms and their architectural applications. Students will also gain hands-on experience through visual programming tools and are expected to develop a term project using ML techniques to address an architectural design problem.
Course Content
The course aims to develop an understanding of machine learning in general terms and its potential relation with computational design and architecture. In this course, firstly the notions such as function, parameter, variable, algorithm, data, problem-space, dimension and features are discussed in relation with design disciplines. The discussion is elaborated with basic introduction to probability and optimization theory and corresponding methods to form a basis for machine learning algorithms. Thereafter, the traditional machine learning strategies, models and algorithms are exemplified with their uses in architecture. Followed by the hands-on tutorial for machine learning implementations in a visual programming language, the students are expected to complete a term-project which focuses on a particular problem in the field of architecture by means of a machine learning algorithm.
Course Learning Outcomes
By the end of the course, students will be able to define key computational concepts and explain their roles in design problem-solving. They will understand the principles of probability and optimization and apply them to guide model selection in data-driven tasks. Students will demonstrate the ability to use Python or visual programming tools for data processing, modeling, and visualization. They will implement machine learning models such as regression, classification, or clustering, and interpret the results in architectural contexts. Additionally, students will formulate a complete ML-based design project and present their findings through visual, verbal, and written formats, reflecting critical thinking and data literacy.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | To be able to implement the knowledge attained throughout the undergraduate education in building science related areas of education, practice, research-development-innovation and to be able to contribute to the developments in these areas | ✔ | |||
| 2 | To be able to define the technical topics and problems, analyze and bring proposals of solution regarding building materials, building physics, construction, construction management, conservation processes and the relationships of the environment and the nature | ✔ | |||
| 3 | To be able to gather and accumulate local and global professional knowledge (theoretical, technical and practical) on building related (building physics, materials, construction, construction details) topics | ✔ | |||
| 4 | To be able to interpret and analyze the information coming from different disciplines regarding buildings, and create new knowledge from them | ✔ | |||
| 5 | To be able to analyze, evaluate and provide solutions to a problem within an ethical framework and with scientific approach | ✔ | |||
| 6 | To be able to propose a research topic or study within an ethical framework, using scientific methods, and with a inter-disciplinary approach/point of view, and develop, evaluate, and run as a research proposal | ✔ | |||
| 7 | To be able to publish the developments and results regarding a research topic in national and international scientific media | ✔ | |||
| 8 | To be able to follow and grasp the new and contemporary technologies / advances in the field of building science | ✔ | |||
| 9 | To be able to follow and interpret the new and current technologies/developments regarding the field of building science | ✔ | |||
| 10 | To be able to have the consciousness of ethical liability in building related professions | ✔ | |||
| 11 | To be able to use the information and communication technologies in an advanced way in the processes of building construction, facilities management, and conservation processes; to be able to use, manage and share the attained knowledge by using these technologies | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution