Jan Dirk Wegner: Catalogue data in Autumn Semester 2022 |
| Name | Prof. Dr. Jan Dirk Wegner (Professor Universität Zürich (UZH)) |
| Address | I. f. Geodäsie u. Photogrammetrie ETH Zürich, HIL D 44.2 Stefano-Franscini-Platz 5 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 68 08 |
| jan.wegner@geod.baug.ethz.ch | |
| Department | Civil, Environmental and Geomatic Engineering |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 851-0650-00L | AI4Good  | 3 credits | 2G | J. D. Wegner | |
| Abstract | The AI4Good course is a hackathon turned into a full course. At the beginning, stakeholders active in the development sector will describe several problems that could be solved with a machine learning approach. Students will spend the semester on designing, implementing, and testing suitable solutions using machine learning. Progress will be discussed with all course members. | ||||
| Learning objective | Given a specific problem in global development, students shall learn to self-responsibly design, implement and experimentally evaluate a suitable solution. Students will also learn to critically evaluate their ideas and solutions together with all course members in a broader context that go beyond mere technical solutions, but touch on ethics, local culture etc., too. | ||||
| Content | The AI4Good course is a hackathon turned into a full course. At the beginning of the course, stakeholders (e.g., NGOs) active in the development sector will describe several problems that could be solved with a machine learning approach. Organizers of the course will make sure that only those problems are selected that are suitable for a machine learning approach and where sufficient amounts of data (and labels) are available. Students will organize themselves into small groups of 3-5 students, where each group works on solving a specific problem. Students will spend the semester on designing, implementing, and testing suitable solutions using machine learning. Every two weeks, each group will present ideas and progress during a short presentation followed by a discussion with all course members. At the end of the course, students will present their final results and submit source code. In addition, they will describe the developed method in form of a scientific paper of 8 pages. Grading will depend on the source code, the paper, and active participation in class. Note: The course AI4Good is not related to Hack4Good, which is a students' initiative organized by the Analytics Club at ETH. For more information about Hack4Good check out the website: https://analytics-club.org/wordpress/hack4good/. | ||||
| Prerequisites / Notice | Students with a strong background in machine learning and excellent programming skills (preferably in Python) | ||||