Note: You do not need to register for the fall school if you have registered for this course. Your participation in the fall school will be free of charge. You can register for the course via HISinOne.
Applying machine learning (ML) and in particular deep learning (DL) in practice is a challenging task and requires a lot of expertise. Among other things, the success of ML/DL applications depends on many design decisions, including an appropriate preprocessing of the data, choosing a well-performing machine learning algorithm and tuning its hyperparameters, giving rise to a complex pipeline. Unfortunately, even experts need days, weeks or even months to find well-performing pipelines and can still make mistakes when optimizing their pipelines.
The lab course will start with a fall school featuring lectures on hot topics in AutoML such as "automating data science", "automated reinforcement learning" or "neural architecture search", as well as present tutorials on various topics in AutoML. After this fall school, students will take on a project themed around Ensembling or capabilities of AutoML Systems.
We require that you have heard a lecture on
- Machine Learning, and/or
- Deep Learning
We strongly recommend that you have taken the AutoML lecture.
Students take part in the AutoML Fall School (remotely, free of charge) to hear from world leading AutoML experts about current hot topics in the field. After this week, we will meet to present potential projects from which the students are free to select which one they want to tackle. We expect that students work in groups of up to three. During the semester the students will meet with a supervisor to discuss potential issues they are facing. At the end of the semester all groups will present their work during a poster presentation.
The grades are determined based on the quality of the project part.
- 24.11.23; MST Pool, Building 74, 14:00-15:00 Introduction to Lab
- 27.11.23 - 30.11.23; MST Pool, Building 74, 09:00-17:30 participation in the AutoML Fall School
- 07.02.24; Room 13, Building 74, 14:00-16:00: Poster presentation