Introduction to Machine Learning Tutorial



Time and place:

  • Thu 8:30-10:00

Fields of study

  • WPF ME-BA-MG6 from SEM 3
  • WPF MT-BA from SEM 5
  • WF CE-BA-TW from SEM 5
  • WPF INF-BA-V-ME from SEM 5
  • WPF INF-BA-V-MI from SEM 5
  • WPF DS-BA from SEM 3
  • WPF IuK-BA from SEM 5
  • WPF ME-MA-MG6 from SEM 1

ECTS information


This course extension is for deepening your knowledge and creating a good basis for lectures in the field of Pattern Recognition. There will be several practical programming tasks, based on the lecture content, which have to be implemented by your own. Passing this course is mandatory to pass the entire module "Introduction to Pattern Recognition". Each programming task is described in detail via stand-alone worksheets being uploaded to StudOn. You have a certain period of time to complete a worksheet and deliver the code by uploading your results to StudOn. You can use the weekly programming exercise slots to ask questions about solving the problems, but all in all, you have to do the work on your own. Of course, it is not allowed to just copy and paste from the web, which will lead to failing the course. Important: In order to successfully complete this course, you have to achieve 50% of the points in EVERY programming task! Teamwork (teams of max. 2 students) is allowed. Of course you can also work on your own.


- lecture notes - H. Niemann: Klassifikation von Mustern - H. Niemann: Pattern Analysis and Understanding - S. Theodoridis and K. Koutroumbas: Pattern Recognition, 4th ed., Academic Press, 2009.

Additional information

Keywords: Mustererkennung, Vorverarbeitung, Merkmalsextraktion, Klassifkation

Expected participants: 250