Problem-based Learning - 2024

This course focuses on the application of machine learning to predict different peptide properties (regression tasks) that are relevant to mass spectrometry-based proteomics [1].
During this course, independent of the assigned prediction task, students are going to:

• perform literature research of a pre-defined topic
• get a general understanding of machine learning and how to apply machine learning to biological data [2,3]
• develop and correctly evaluate a machine learning model including parameter optimization [2,3]
• present milestones and final results in various presentations to the other students and supervisors
• summarize results in a paper-like scientific report at the end of the course
• gain a first look at the peer-review process by critically evaluating the reports of other students

Students will work in groups of ~3. They will present their topic, background as well as their datasets together. They will work on the same prediction task, but will follow different approaches as discussed with their supervisor. In the end, each group will merge their result, present them and and final conclusion in a talk and a written scientific report.