FACULTY OF FINE ARTS AND DESIGN

Department of Architecture

ARCH 362 | 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;
  • Will be able to analyze data for drawing insights through machine learning,
  • Will be able to sort architectural knowledge based on acquired data
  • Will develop an improved skill level in applying machine learning and deep learning models for their architectural practice
  • Will be able to use at least one basic software needed for AI in Architecture.
  • Will be able to preprocess data to enable machine learning or deep learning processes in AI in Architecture.
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.

 



Course Category

Core Courses
Major Area Courses
X
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
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
  • Nielsen, M. (2017) Neural Networks and Deep Learning, Online book @ neuralnetworksanddeeplearning.com
  • Goodfellow. I., Bengio Y., Courville A. (2016) Deep Learning, MIT Press @ www.deeplearningbook.org
  • Autodesk University
Suggested Readings/Materials
  • Steenson, M. W. (2017) Architectural Intelligence: How Designers and Architects Created the Digital Landscape, The MIT Press, Cambridge, Massachusettes
  • Hyde, R. (2016) Architecture in the coming age of Artificial Intelligence Retrieved from https://architectureau.com/articles/architecture-in-the-coming-age-of-artificial-intelligence/ 04.04.2018
  • Oxman, R., Oxman. R. (2014) Theories of the Digital in Architecture, Routledge New York, NY
  • Hall, J. Storrs. (2007) Beyond AI: Creating the Conscience of the Machine. Amherst, NY: Prometheus Books, 253.
  • Negroponte, N. (1975) Soft Architecture Machines, The MIT Press. Cambridge, Massachusettes. Available at: http://www.uni-due.de/~bj0063/doc/Negroponte.pdf

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
30
Presentation / Jury
Project
1
30
Seminar / Workshop
Oral Exams
Midterm
2
30
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

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
5
80
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
0
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
1
16
16
Presentation / Jury
0
Project
1
4
4
Seminar / Workshop
0
Oral Exam
0
Midterms
2
5
10
Final Exam
0
    Total
110

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
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

 


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