| Course Name | Architectural Intelligence: Artificial Intelligence (AI) in Architecture |
| Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
|---|---|---|---|---|---|
| ARCH 362 | Fall/Spring | 2 | 2 | 3 | 4 |
| Prerequisites | None | |||||
| Course Language | English | |||||
| Course Type | Elective | |||||
| Course Level | First Cycle | |||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | This course will explore Artificial Intelligence concepts that are converging with the fundamentals and the practice of Architecture. In this course the student will develop an understanding of Deep Learning applications in Architectural domains. The course will be based on exploring the Architectural Intelligence that is embedded in the tacit experience of its practitioners and within the built environment. Assignments will be on applying machine learning and deep learning models on available data concerning built spaces. Skills attained in this course are expected to help prospective architecture professionals in creation and evaluation and feedback processes of architectural spaces. |
| Learning Outcomes | The students who succeeded in this course;
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| Course Description | Throughout the semester, the students will be introduced to basic concepts of Artificial Intelligence (AI). Students will be exploring advanced state-of-the-art applications of AI in various scales within the scope of lectures given during the first hour of each class. Weekly assignments will give students the opportunity for hands-on experience with data processing, machine learning and deep learning models. A project will run from mid-semester to the Final. |
| Related Sustainable Development Goals |  |
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| Core Courses | |
| Major Area Courses | X | |
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Required Materials |
| 1 | Syllabus Overview: Introduction, Attendance and Time Keeping | Introduction |
| 2 | Basics of Artificial Intelligence (AI) | Research and daily tasks |
| 3 | History of AI, Machine Learning and Deep Learning | Research and daily tasks |
| 4 | Computation and Creativity in Architecture | Research and daily tasks |
| 5 | Architecture and Patterns | Research and daily tasks |
| 6 | Overview of Deep Learning Models | Research and daily tasks |
| 7 | Data Acquisition, Preprocessing, Processing, Generative AI I | Research and daily tasks |
| 8 | Generative AI II | Research and daily tasks |
| 9 | Midterms Week | |
| 10 | Computer Vision (CV) Basics | Research and daily tasks |
| 11 | Quiz I_Workshop | Building Learning Models |
| 12 | Ethics of AI | Work on AI projects |
| 13 | Quiz II_Workshop | |
| 14 | Advances in BIM towards AI | Work on AI projects |
| 15 | AI and Robotics in Construction | Work on AI projects |
| 16 | Final Project Presentations | Work on AI projects and presentations |
| Course Notes/Textbooks | |
| Suggested Readings/Materials |
|
| Semester Activities | Number | Weighting |
| Participation | 1 | 10 |
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | 2 | 30 |
| Portfolio | ||
| Homework / Assignments | 1 | 30 |
| Presentation / Jury | ||
| Project | 1 | 30 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | ||
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade | 4 | 70 |
| Weighting of End-of-Semester Activities on the Final Grade | 1 | 30 |
| Total |
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 4 | 64 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
| Study Hours Out of Class | 0 | ||
| Field Work | |||
| Quizzes / Studio Critiques | 2 | 4 | |
| Portfolio | |||
| Homework / Assignments | 8 | 4 | |
| Presentation / Jury | |||
| Project | 1 | 6 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | |||
| Final Exams | |||
| Total | 110 |
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | To be able to offer a professional level of architectural services. | X | ||||
| 2 | To be able to take on responsibility as an individual and as a team member to solve complex problems in the practice of design and construction. | X | ||||
| 3 | To be able to understand methods to collaborate and coordinate with other disciplines in providing project delivery services.
| X | ||||
| 4 | To be able to understand, interpret, and evaluate methods, concepts, and theories in architecture emerging from both research and practice. | X | ||||
| 5 | To be able to develop environmentally and socially responsible architectural strategies at multiple scales. | X | ||||
| 6 | To be able to develop a critical understanding of historical traditions, global culture and diversity in the production of the built environment. | |||||
| 7 | To be able to apply theoretical and technical knowledge in construction materials, products, components, and assemblies based on their performance within building systems. | |||||
| 8 | To be able to present architectural ideas and proposals in visual, written, and oral form through using contemporary computer-based information and communication technologies and media. | X | ||||
| 9 | To be able to demonstrate a critical evaluation of acquired knowledge and skills to diagnose individual educational needs and direct self-education skills for developing solutions to architectural problems and design execution. | X | ||||
| 10 | To be able to take the initiative for continuous knowledge update and education as well as demonstrate a lifelong learning approach in the field of Architecture. | X | ||||
| 11 | To be able to collect data in the areas of Architecture and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1) | X | ||||
| 12 | To be able to speak a second foreign language at a medium level of fluency efficiently. | |||||
| 13 | To be able to relate the knowledge accumulated throughout the human history to their field of expertise. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
