Institute for Web Science and Technologies · Universität Koblenz - Landau

Machine Learning and Data Mining

[zur Übersicht]

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 optimization and deep learning. Excited to participate? Then, you need to:

  • Register to the lecture in Klips
  • Register to one tutorial in Klips
  • Add yourself into a group for working on mandatory assignments here

For inter-student communication, please use the newsgroup infko-mldm.

Important Information

To whome?

Master and Bachelor students in:

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


Exam: !coming soon!

Organizational Information

Lecture (Klips)

  • Lecturer: Dr. Zeyd Boukhers
  • Wednesdays, 08:00 - 10:00 in M 001
  • Consultation: Mondays at 09:00 in B114 or by appointment (

Tutorial (Klips)

  • Instructors: Akram Sadat Hosseini, Raphael Menges and Qusai Ramadan
  • Thursdays, 14:00 - 16:00, G 210
  • Thursdays, 16:00 - 18:00, C 206
  • You don't need to come to both slots for the tutorial. We will cover the same material on both.

Please form groups of four people to work on the assignments here, until 25th of October! The assignments are graded before the next tutorial and it is mandatory to reach 60% of the points in total over all assignments to be allowed to participate in the exam. E.g., if there are 10 assignments each 10 points, you need in total at minimum 60 points in sum over all assignments to participate in the exam. Other information will be published soon!

Course Material

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

Video Lectures

Lecture recordings will be provided along with the progress of the lecture.


Assignments will be provided along with the progress of the tutorials


  • Wissenschaftlicher Mitarbeiter
  • B 114
  • +49 261 287-2765
  • Wissenschaftlicher Mitarbeiter
  • B 108
  • +49 261 287-2862
  • Wissenschaftlicher Mitarbeiter
  • B 007
  • +49 261 287-2966