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COURSE INTRODUCTION AND APPLICATION INFORMATION
| 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 | 1 | 4 | 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;
|
| Course Description | Throughout the semester, the students will be introduced to basic concepts of Artificial Intelligence (AI). Students will be exploring advances 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. |
|
| Core Courses | |
| Major Area Courses | X | |
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Required Materials |
| 1 | Syllabus overview: introduction, attendance and time keeping. | Introduction + Assignment #1 |
| 2 | Basics of AI | Assignment #2: understanding data |
| 3 | History of AI, Machine Learning and Deep Learning | Assignment #3: classification |
| 4 | Computation in Architecture, Nicholas Negroponte, William J. Mitchell et.al. | Assignment #4: Goodfellow. I., et.al. (2016) Deep Learning, MIT Press @ www.deeplearningbook.org |
| 5 | Architecture and Patterns, Shape Grammars. Works of Christopher Alexander, George Stiny, John S. Gero et.al | Assignment #5:Text processing, Image processing |
| 6 | Midterm I | |
| 7 | Overview of Deep learning models | Assignment #6: Nielsen, M. (2017) Neural Networks and Deep Learning, Online book |
| 8 | Data Acquisition | Assignment #7 |
| 9 | Data Preprocessing basics | Assignment #8 |
| 10 | Computer Vision(CV) basics | Work on Project CV |
| 11 | Building Learning Models | Work on Project |
| 12 | Midterm II | |
| 13 | Advances in BIM towards AI | Work on Project |
| 14 | Project Presentations | Work on Project |
| 15 | Project Presentations | Work on Project/ Presentation |
| 16 | Final, Project Presentations | Work on Project/ Presentation |
| Course Notes/Textbooks |
|
| Suggested Readings/Materials |
|
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | 16 | 10 |
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | 8 | 30 |
| Presentation / Jury | ||
| Project | 1 | 30 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | 2 | 30 |
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade | 27 | 100 |
| Weighting of End-of-Semester Activities on the Final Grade | ||
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 1 | 16 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | 4 | |
| Study Hours Out of Class | |||
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Portfolio | |||
| Homework / Assignments | 8 | 2 | |
| Presentation / Jury | |||
| Project | 1 | 4 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | 5 | ||
| Final Exams | |||
| Total | 100 |
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | Ability to apply theoretical and technical knowledge in architecture. | X | ||||
| 2 | Ability to understand, interpret and evaluate architectural concepts and theories. | X | ||||
| 3 | Ability to take on responsibility as an individual and as a team member to solve complex problems in the practice of architecture.
| X | ||||
| 4 | Critical evaluation of acquired knowledge and skills to diagnose individual educational needs and to direct self-education. | X | ||||
| 5 | Ability to communicate architectural ideas and proposals for solutions to architectural problems in visual, written and oral form. | X | ||||
| 6 | Ability to support architectural thoughts and proposals for solutions to architectural problems with qualitative and quantitative data and to communicate these with specialists and non-specialists. | X | ||||
| 7 | Ability to use a foreign language to follow developments in architecture and to communicate with colleagues. | X | ||||
| 8 | Ability to use digital information and communication technologies at a level that is adequate to the discipline of architecture. | X | ||||
| 9 | Being equipped with social, scientific and ethical values in the accumulation, interpretation and/or application of architectural data. | X | ||||
| 10 | Ability to collaborate with other disciplines that are directly or indirectly related to architecture with basic knowledge in these disciplines. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest