Iro Armeni: Catalogue data in Spring Semester 2022
|Name||Dr. Iro Armeni|
|Department||Civil, Environmental and Geomatic Engineering|
|101-0526-00L||Introduction to Visual Machine Perception for Architecture, Construction and Facility Management||3 credits||2G||I. Armeni|
|Abstract||The course is an introduction to Visual Machine Perception technology, and specifically Computer Vision and Machine Learning, for Architecture, Construction, and Facility Management (ACFM). It will explore fundamentals in these Artificial Intelligence (AI) technologies in a tight reference to three applications in ACFM, namely architectural design, construction renovation, and facility management.|
|Objective||By the end of the course students will develop computational thinking related to visual machine perception applications for the ACFM domain. Specifically, they will:|
-Gain a fundamental understanding of how this technology works and the impact it can have in the ACFM industry by being exposed to example applications.
-Be able to identify limitations, pitfalls, and bottlenecks in these applications.
-Critically think on solutions for the above issues.
-Acquire hands-on experience in creatively thinking and designing an application given a base system.
-Use this course as a “stepping-stone” or entry-point to Machine Learning-intensive courses offered in D-BAUG and D-ARCH.
|Content||The past few years a lot of discussion has been sparked on AI in the Architecture, Construction, and Facility Management (ACFM) industry. Despite advancements in this interdisciplinary field, we still have not answered fundamental questions about adopting and adapting AI technology for ACFM. In order to achieve this, we need to be equipped with rudimentary knowledge of how this technology works and what are essential points to consider when applying AI to this specific domain.|
In addition, the availability of sensors that collect visual data in commodity hardware (e.g., mobile phone and tablet), is creating an even bigger pressure in identifying ways that new technology can be leveraged to increase efficiency and decrease risk in this trillion-dollar industry. However, cautious and well-thought steps need to be taken in the right direction, in order for such technologies to thrive in an industry that showcases inertia in technological adoption.
The course will unfold as two parallel storylines that intersect in multiple places:
1) The first storyline will introduce fundamentals in computer vision and machine learning technology, as building blocks that one should consider when developing related applications. These blocks will be discussed with respect to latest developments (e.g., deep neural networks), pointing out their impact in the final solution.
2) The second storyline consists of 3 ACFM processes, namely architectural design, construction renovation, and facility management. These processes will serve as application examples of the technological storyline.
In the points of connection students will see the importance of taking into account the application requirements when designing an AI system, as well as their impact on the building blocks. Guest speakers from both the AI and ACFM domains will complement the lectures.
|Prerequisites / Notice||The course does not require any background in AI, Computer Science, coding, or the ACFM domain. It is designed for students of any background and knowledge on these topics. Despite being an introductory class, it will still engage advanced students in the aforementioned topics.|
|263-5904-00L||Deep Learning for Computer Vision: Seminal Work |
Number of participants limited to 24.
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
|2 credits||2S||I. Armeni|
|Abstract||This seminar covers seminal papers on the topic of deep learning for computer vision. The students will present and discuss the papers and gain an understanding of the most influential research in this area - both past and present.|
|Objective||The objectives of this seminar are two-fold. Firstly, the aim is to provide a solid understanding of key contributions to the field of deep learning for vision (including a historical perspective as well as recent work). Secondly, the students will learn to critically read and analyse original research papers and judge their impact, as well as how to give a scientific presentation and lead a discussion on their topic.|
|Content||The seminar will start with introductory lectures to provide (1) a compact overview of challenges and relevant machine learning and deep learning research, and (2) a tutorial on critical analysis and presentation of research papers. Each student then chooses one paper from the provided collection to present during the remainder of the seminar. The students will be supported in the preparation of their presentation by the seminar assistants.|
|Lecture notes||The selection of research papers will be presented at the beginning of the semester.|
|Literature||The course "Machine Learning" is recommended.|