Problem Based Learning SoSe 2020

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

News (newest info always at the top)

The 2nd milestone talks will take place on Monday, 02.11, 12:00-14:00. Due to the current situation, this meeting will be online. The meeting information can be found on Piazza.

The kick-off meeting will take place on Monday, 27.04, 12:00-14:00. See more information below.

Due to the current situation, we are going to offer this course online for the time being. We are currently exploring options on how to do that best and will update all students signed up to this course as soon as possible.

Communication will mostly happen over Piazza. You can enroll using: piazza.com/tum.de/spring2020/pbl.

More detailed descriptions of the topics will be published one week before the kickoff meeting. 2-3 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, 27.04, 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 17.

Meetings

Date   Students Supervisors
27.04.2020 Kick-off Meeting Everyone Maria Littmann
11.05.2020 How to give a good presentation Everyone Maria Littmann
08.06.2020 Introduction Talks

Benjamin, Katja, Julian

Zhuoyun, Sarah O.

Christian Dallago,

Michael Heinzinger

15.06.2020 Introduction Talks

Sara D., Leon, David

Michael Bernhofer

22.06.2020 Introduction Talks

Laurens, Kok Yuan

Pia

Arina, Chiara, Mariam

Maria Littmann,

Maria Littmann,

Konstantin Weißenow

29.06.2020 Introduction to Machine Learning in Python Everyone Maria Littmann
06.07.2020 How to evaluate a Machine Learning predictor Everyone Maria Littmann
20.07.2020 First milestone talks Everyone Everyone
02.11.2020 Second milestone talks Everyone Everyone
09.11.2020 Scientific Writing Everyone Maria Littmann
30.11.2020 Final talks

Benjamin, Julian

Stefan, Laurens

Christian Dallago,

Maria Littmann

07.12.2020 Final talks

Grace, Sarah

Pia

Michael Heinzinger,

Maria Littmann

14.12.2020 Final talks

Arina, Chiara, Mariam

Leon, David

Konstantin Weißenow,

Michael Bernhofer

Slides

27.04.2020: Kick-off Meeting

11.05.2020: How to give a good presentation

29.06.2020: Introduction to Machine Learning in Python; Jupyter Notebook

06.07.2020: How to evaluate a Machine Learning predictor

09.11.2020: Tips on scientific writing

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

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.