TR
11111

COURSE INTRODUCTION AND APPLICATION INFORMATION

mmr.fadf.ieu.edu.tr

Course Name
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
Fall/Spring
Prerequisites
None
Course Language
Course Type
Elective
Course Level
-
Mode of Delivery -
Teaching Methods and Techniques of the Course
Course Coordinator -
Course Lecturer(s)
Assistant(s) -
Course Objectives
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

 



Course Category

 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
  • 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
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

 

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