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AI for Existing Buildings
In recent years, Artificial Intelligence (AI) has emerged as an integral aspect of modern life. It permeates our daily interactions, from our personal devices to the systems we encounter in various environments. Moreover, AI has recently significantly expanded its influence within the disciplines of architecture and historic preservation, introducing novel perspectives on how we engage with the built environment and fostering innovative opportunities.
This course aims to provide a ten-thousand-foot overview of AI, offering insights into some of the most commonly employed AI and machine learning (ML) concepts in existing buildings with cultural and historical significance. By delving into some of the fundamental principles, we seek to empower students not only as mere users of these technologies but as proficient applicators capable of seamlessly integrating ML into their future workflows. To enable this, we will provide comparative examples of how traditional workflows in the adaptation and preservation of existing buildings can be supplanted and bolstered by ML technologies to make them more efficient and effective. Ultimately, we want to enable students to unleash their potential for innovation at a whole new level.
Students are not expected to know Python programming language to take the course. We will teach programming basics and provide template codes to the students that can be easily adapted to the use cases necessary to accomplish the project.
Preservation Technology Lab (655 Schermerhorn)
W 6 PM - 8 PM
Session B
13583
| Course | Semester | Title | Student Work | Instructor | Syllabus | Requirements & Sequence | Location & Time | Session & Points | Call No. |
|---|---|---|---|---|---|---|---|---|---|
| A6921‑1 | Fall 2024 |
Machine Learning
|
Bilge Kose, Kivanc Kose |
Preservation Technology Lab
W 5:30 PM - 7:30 PM
|
SES B
1.5 Points
|
10610 | |||
| A6921‑1 | Fall 2023 |
Machine Learning
|
|
Bilge Kose, Kivanc Kose |
Preservation Technology Lab
W 5:30 PM - 7:30 PM
|
SES B
1.5 Points
|
10117 | ||
| ARCH6921‑1 | Fall 2022 |
Machine Learning
|
Özgün Balaban |
9/19 - 9/30, M/T/TH/F, 6:30-8:30 PM
|
9/19 - 9/30
1.5 Points
|
13209 |
