Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, - Vol.23 - 16th DDAS (MSTAS) Special Issue -2022, 60 - 67, 23.12.2022
https://doi.org/10.18038/estubtda.1169816

Öz

Kaynakça

  • [1] Khean N, Fabbri A & Haeusler MH. Learning Machine Learning as an Architect, How to? Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018; 95-102
  • [2] Carpo, M. The Second Digital Turn - Design Beyond Intelligence, MIT Press, Cambridge, 2017
  • [3] Mueller C & Danhaive R. Special Subject: Building Technology - Machine Learning for Creative Design | MIT Architecture. Retrieved April 22, 2022, from https://architecture.mit.edu/subject/spring-2019-4s42, 2019
  • [4] León, D. A. Artificial Intelligence in Architecture: MaCAD Students Learning How to Radically Innovate the AEC Sector. IAAC. Retrieved April 22, 2022, from https://iaac.net/artificial-intelligence-macad; 2021
  • [5] del Castillo y Lopez, JLG. Machine Learning, and the Built Environment. Retrieved April 22, 2022, from https://pll.harvard.edu/course/ai-machine-learning-and-built-environment, 2022
  • [6] Miller C. Data Science for Construction, Architecture and Engineering. edX. Retrieved April 22, 2022, from https://www.edx.org/course/Data-Science-for-Construction-Architecture-and-Engineering, 2022
  • [7] City of New York. NYC Open Data. Retrieved April 22, 2022, from https://opendata.cityofnewyork.us/data
  • [8] Lunchbox. Proving Ground Apps. https://apps.provingground.io/lunchbox. Published June 2, 2021. Accessed May 05, 2022,
  • [9] GitHub. https://github.com. Accessed May 05, 2022.

DEMYSTIFYING MACHINE LEARNING FOR ARCHITECTURE STUDENTS

Yıl 2022, - Vol.23 - 16th DDAS (MSTAS) Special Issue -2022, 60 - 67, 23.12.2022
https://doi.org/10.18038/estubtda.1169816

Öz

With the developments in technology, mass data, new approaches/tools, and the increasing inclusion of machine learning applications, the necessity to teach these concepts and their applications have emerged in all research areas including architecture. In this context, a new course named “machine learning applications in architecture” containing lectures on data, data literacy, patterns, and various kinds of models along with a project conducted by the students was developed and started to be taught in spring’2020. Conducting a class on relatively new subjects for students was a great challenge. Yet, with a well-defined problem-based learning approach, the adaptation of students to the subject took place immediately. It is important to note that as students are equipped with information on machine learning concepts and applications with the given lectures, they were free to choose the project topics of their own which are believed to be one of the reasons for the success of the end results.
As a result of this class, the project topics varied widely as coloring a given painting, predicting the era of a building, interpreting 2d drawings for 3d modeling, optimizing daylight gain, analyzing distinctive features of data in a city, and visualizing data to represent various aspects in data. The outcomes of the class are documented and analyzed to show how information in different fields such as computer science, engineering, statistics, and so on can broaden their thinking of how to attack problems in the architectural design domain. Finally, topics such as data, data literacy, pattern recognition, and intelligent models are projected to play a key role in the future of design education since it provides an interdisciplinary ground to think about problems at hand from a distinct perspective.

Kaynakça

  • [1] Khean N, Fabbri A & Haeusler MH. Learning Machine Learning as an Architect, How to? Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018; 95-102
  • [2] Carpo, M. The Second Digital Turn - Design Beyond Intelligence, MIT Press, Cambridge, 2017
  • [3] Mueller C & Danhaive R. Special Subject: Building Technology - Machine Learning for Creative Design | MIT Architecture. Retrieved April 22, 2022, from https://architecture.mit.edu/subject/spring-2019-4s42, 2019
  • [4] León, D. A. Artificial Intelligence in Architecture: MaCAD Students Learning How to Radically Innovate the AEC Sector. IAAC. Retrieved April 22, 2022, from https://iaac.net/artificial-intelligence-macad; 2021
  • [5] del Castillo y Lopez, JLG. Machine Learning, and the Built Environment. Retrieved April 22, 2022, from https://pll.harvard.edu/course/ai-machine-learning-and-built-environment, 2022
  • [6] Miller C. Data Science for Construction, Architecture and Engineering. edX. Retrieved April 22, 2022, from https://www.edx.org/course/Data-Science-for-Construction-Architecture-and-Engineering, 2022
  • [7] City of New York. NYC Open Data. Retrieved April 22, 2022, from https://opendata.cityofnewyork.us/data
  • [8] Lunchbox. Proving Ground Apps. https://apps.provingground.io/lunchbox. Published June 2, 2021. Accessed May 05, 2022,
  • [9] GitHub. https://github.com. Accessed May 05, 2022.
Toplam 9 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Arzu Sorguç 0000-0001-9603-0340

Müge Kruşa Yemişçioğlu 0000-0003-4417-0776

Ozan Yetkin 0000-0002-3843-6972

Yayımlanma Tarihi 23 Aralık 2022
Yayımlandığı Sayı Yıl 2022 - Vol.23 - 16th DDAS (MSTAS) Special Issue -2022

Kaynak Göster

AMA Sorguç A, Kruşa Yemişçioğlu M, Yetkin O. DEMYSTIFYING MACHINE LEARNING FOR ARCHITECTURE STUDENTS. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. Aralık 2022;23:60-67. doi:10.18038/estubtda.1169816