In this semester, we will work towards a small collaborative online game (a distributed and barrier-free version of Stone, Papers, Scissors) involving a HTML/JavaScript client that includes Machine Learning (ML)-powered technology to detect gestures or speech from a webcam image or your microphone, and a simple WebSocket server for communication between clients. The basic idea is that you should be able to play against each other with hand gestures and/or speech.

Technology-wise, we will take closer look at Google Teachable Machine and tensorflow.js, and how those technologies can be integrated with a Vue.js application. We will approach our final goal in small steps. In the first part of the course, there will be weekly assignments that lead you to a deeper understanding of the technology and its use cases, in the second part, we will work on the actual game client implementation.

Participants will have to work on their own.

Participation in 80% of the sessions is mandatory. Grades are based on participation in class (20%), weekly assignments (40%), and the presentation of the game client (40%).

Regarding the CDCLab integration:

If you want to be graded within CDCLab and have a concrete use case for the technologies discussed in the course within your project, you can do that instead of implementing the game client (which would also require an implementation overhead that is comparable to the game client for each group member). You will have to pitch the idea to me if you want to do it like that.

Otherwise, you can work on the game client in my course and use parts of the technology or reflect critically on it within the CDCLab presentation.