As DDS team we are offering two new courses for the next semester (Spring, 2019-2020): “Introduction to Machine Learning in Architecture” and “Nature-informed Computational Design in Architecture“. We are looking forward to experience these journeys with our students and faculty.
BS 723 | Introduction to Machine Learning in Architecture |
BS 724 | Nature-informed Computational Design in Architecture |
| The course aims to develop an understanding of machine learning in general terms and its potential relation with computational design and architecture. In this course, firstly the notions such as function, parameter, variable, algorithm, data, problem-space, dimension and features are discussed in relation with design disciplines. The discussion is elaborated with basic introduction to probability and optimization theory and corresponding methods to form a basis for machine learning algorithms. Thereafter, the traditional machine learning strategies, models and algorithms are exemplified with their uses in architecture. Followed by the hands-on tutorial for machine learning implementations in a visual programming language, the students are expected to complete a term-project which focuses on a particular problem in the field of architecture by means of a machine learning algorithm. | This is an experimental computational design research course. This course outlines the basic knowledge on nature-informed computational design which is a rapidly developing approach not only in architecture but in many disciplines. In this course along with many well-known approaches, some of the significant examples will be studied in the context of data and act of modelling which are the state-of-art discussions of the computational design today. By conducting a hands-on project and providing a research paper on the project, it is aimed to enable students to obtain not only the targeted knowledge and skills, but also the critical point of view to the field. |