Institute for Web Science and Technologies · Universität Koblenz
Institute WeST
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Research Lab "Machine Learning Application"

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Winter Term 2021 / 2022

In this research lab, you are going to build a complete machine learning system following the generic pipeline in order to solve a specific problem in the healthcare domain. For each phase in this pipeline, you will adopt the methods and techniques being studied in Machine Learning and Data Mining course. Therefore, completing this lecture is mandatory. Moreover, other fundamental approaches will also be used when necessary [1], including other sophisticated and modified approaches from the state-of-the-art.

Important Information


Master and Bachelor students in:

  • Web Science
  • Computer Science
  • Computational Visualistics
  • Business Informatics

Kick-off / Introductory meeting

  • When: September 22 at 10:00 (your attendance is mandatory).
  • Where: Online via BBB (A link will be sent to registered students only)

How to register?

  • Form a group of four people to work on a topic
  • Give a name to your group
  • Send (one) email to with the subject: "MLA registration request" no later than September 17. Please include your MLDM grade in the email.
  • Attend the introductory meeting
  • After the topic is assigned, write a proposal (up to two pages), describing your potential solution.
  • Register to the exam

Important note: If you could not form a group, you may still take part in the research lab. However, you will have to work with other people who couldn’t form groups.

Note that only 12 students will be selected based on their grade in MLDM and in case of similar grades, first come first serve.


  • When:----
  • Where:----
  • Type: Presentation + Report + Software
  • Registration (Klips): Open from ---- to ---- (Do Not miss the deadline!)
  • Cancellation (Klips): Until ----


We will discuss them during the kick-off meeting.


[1] Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1-38.


  • Alumnus
  • B 104
  • +49 261 287-2765