Problem Based Learning SoSe 2021/WiSe 2021/22

Type 5S (1 year module, 2  SWS/Summer, 3 SWS/Winter)
ECTS 9
Lecturer Burkhard Rost, Maria Littmann, Kyra Erckert
Time Monday, 12:00 - 14:00
Room MI 01.09.034 / online
Language English

News (newest info always at the top)

The slides for the kick-off meeting and topic descriptions are online. More detailed information regarding the course has been sent to all students registered for PBL2021/22 via e-mail.

More detailed descriptions of the topics will be published in the week before the kickoff meeting. 3-4 students will work on the same topic, but will focus on different aspects of the given problem.

Kick-off meeting

The kick-off meeting will take place online on Monday, 12.04, 12:00-14:00. Attendance is mandatory. All important information including detailed description of the topcis will be sent out via e-mail to all registered participants by April 8.

Meetings

Date   Students Supervisors
12.04.2021 Kick-off Meeting Everyone Everyone
26.04.2021 How to give a good presentation (Lecture Talk) Everyone Littmann, Erckert
31.05.2021 Introduction Talks Elias K., Maximilian, Julius, Selin Weissenow
07.06.2021 Introduction Talks

Lisa, Felix, Johanna

Elias A., Erik, Simon, Denise

Marquet

Littmann

14.06.2021 Introduction Talks

Fabio, Moritz, Annette, Ningyue

Samantha, Timo, Maksim, Mathias

Erckert

Littmann

21.06.2021 Introduction to Machine Learning & Python (Lecture Talk) Everyone Littmann, Erckert
12.07.2021 First Milestone Talks Everyone Everyone

Slides

12.04.2021: Kick-off meeting

Please enroll to Piazza to access all further material.

Course outline and goals

This course focuses on the application of machine learning to predict various aspects of protein function and structure. 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
  • develop and correctly evaluate a machine learning model including parameter optimization (using Python 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

Students will work in groups. They will present their topic and biological background as well as their dataset 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 and will present the results and a final conclusion in a talk and a written scientific report.

Topics (4 students, one group with 3 students)

Material

For your final report, please use the Bioinformatics template

Use a numbered citation style listing references in the order they appear in the text, i.e. the first reference in the text has number 1. Citations in the text should appear as (1) or [1]. Don't use footnotes but include the references at the end of the text.