Institute for Web Science and Technologies · Universität Koblenz
Institute WeST
This course is from a past or future semester. If you are looking for current courses, go to the course overview.

Machine Learning and Data Mining

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Winter Term 2021 / 2022

The course “Machine Learning and Data Mining (MLDM)” covers the fundamentals and basics of machine learning and data mining. The course provides an overview of a variety of MLDM topics and related areas such as clustering and classification.

Excited to participate? Then, you need to:

  1. Register to the lecture in KLIPS. Mandatory for all students who want to participate. We will add only registered students to the course in Olat
  2. You will have access to the course in OLAT the next Friday at noon (until November 12). If you register on Friday after 12:00 pm, we will grant you access the next Friday. Therefore, make sure to register early so that you do not miss anything!

Please use the forum on the OLAT platform for general questions about the course. You may send E-Mails only for urgent matters or when it is about private data.

Info: Exam admissions from the Machine Learning and Data Mining course of the winter term 2020/2021 are still valid, regardless of whether you participated in an exam. However, students who attended the course one year ago and scored at least 60% of the points in the assignments may also take part in the assignments this semester.

First exam: Will be announced later
Second exam: Will be announced later

Important Information

Who can participate?

Master and Bachelor students in:

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


Course Material

Slides, lecture recordings and additional material will be provided along with the progress of the lecture.


Tutorials will discuss the solutions to the last assignment and discuss the exercises of the current one. Solutions will not be uploaded!


Assignments will be provided along with the progress of the lectures.



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