052-0630-23L CAAD Practice: Topic
| Semester | Frühjahrssemester 2023 |
| Dozierende | L. Hovestadt |
| Periodizität | jedes Semester wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
| Kommentar | ITA Pool information event on the offered courses: 8.2.23 (10-11 h), ONLINE: Link will follow. |
Lehrveranstaltungen
| Nummer | Titel | Umfang | Dozierende | ||||
|---|---|---|---|---|---|---|---|
| 052-0630-23 G | CAAD Practice: Topic
 No course on 20.3. (seminar week) and in the last two weeks of the semester (final critiques). | 2 Std. |
| L. Hovestadt |
Katalogdaten
| Kurzbeschreibung | The course introduces the interplay between machine learning and the architectural design process. It is about how architects can navigate a probabilistic design space and what kind of questions to ask. It also shows how integrating learning from data to discover patterns and holistically manipulate the data entities with design. |
| Lernziel | - Students learn an introduction of an applicative perspective of using machine learning in design. - Students learn how feature engineering can be connected to the design elements and how that improves the quality of results. - confronting the digital modeling with the “thinking” of machine learning. - Students can use geometry as an expression to visualize and conceive the machine learning process. - Connect these geometric expressions with architectural design elements in informational spaces. - The course starts with introductory lectures with intuitive tools and finishes with individual experiments to design architectural models. |
| Inhalt | This course shows the complex relativity between the data’s sharp borders and its boundaries, where morphing anything is possible, and anything may be related or connected. Architects can manipulate and design the feature engineering algorithms of machine learning and connect them to the design process to improve the quality of results. Basic introduction of Rhino and Grasshopper will be instructed to use the basic interfaces’ functions. Coded files with examples will be used throughout the course. - Information Space. - Read geometry as particles and waves. - Implicit vs parametric. - Machine learning aided design. - Embody values of high dimensional elements. - Obstacles and distance measurements. - What is the knowledge of needed tools inside Rhino and Grasshopper for this course? - Rhino Interface, use basic drawing commands (Point, Line, Surface, Extrude, Solids Boolean operations, Meshes). - Grasshopper Interface, know basics (connecting components, assign geometry from rhino, basic components, Tree and lists, export). - Introduction - ANN - Self-organizing map – Principle component analysis. - Non-linear morph between geometries. - Generate in-between design alternatives. - Design a conceptual building form in a probabilistic space. |
| Skript | To follow |
| Literatur | http://www.caad.arch.ethz.ch |
Leistungskontrolle
| Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
| ECTS Kreditpunkte | 2 KP |
| Prüfende | L. Hovestadt |
| Form | unbenotete Semesterleistung |
| Prüfungssprache | Englisch |
| Repetition | Repetition nur nach erneuter Belegung der Lerneinheit möglich. |
| Zusatzinformation zum Prüfungsmodus | Es wird in Englisch UND Deutsch unterrichtet. |
Lernmaterialien
| Hauptlink | Information |
| Es werden nur die öffentlichen Lernmaterialien aufgeführt. | |
Gruppen
| Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
| Allgemein |  : Für Fachstudierende und Hörer/-innen ist eine Spezialbewilligung der Dozierenden notwendig |
Angeboten in
| Studiengang | Bereich | Typ | |
|---|---|---|---|
| Architektur Bachelor | Technologie in der Architektur | W |  |