- When: Wed 09.00 – 11.00 c.t.
- Where: Lab and online
- 1st meeting: October 5th, 2022, 09:15, Seminar Room FAV 01 A!
This semester ‘s focus is “explainable and trustworthy AI”. Students are encouraged to work on tools for measuring and mitigating bias, assessment strategies like Revise (A Tool for measuring bias in visual datasets) , Aequitas (web application to interactively audit datasets), AI Fairness 360 or others.
On the other hand this course also encourages students to select and implement a computer vision project of their choice. Students are free to develop in any programming language they like and to use any publicly available library they want. The only requirement is that the effort for developing the chosen application is in line with the ECTS of this course. The goal is to encourage students to investigate a selected computer vision topic in detail, and to allow them to improve their computer vision programming skills. Some examples are presented as part of the first lecture, and the lecturers are happy to help participants choose their topics. It is also possible to implement an internship topic, partially or fully in groups.
This is not a general programming course; students are expected to be able to develop software on their own, and they should be familiar with a programming language suitable for computer vision development (e.g. Matlab, Python, C++). Basic image processing and computer vision knowledge is expected. Experience in computer vision development is recommended but not required.
Project 1: tbd
Project 2: tbd
Project 3: tbd
Project 4: tbd
Project 5: tbd
The initial, midterm, and final presentations account for 5%, 5% and 10% of the grade, respectively. The project, which must include a written report, is worth 80% of the grade.
We recommend the associated lecture that covers software and resources for computer vision development as well as selected computer vision applications.
see in TISS: 183.586 Computer Vision Systems Programming –> Einzeltermine