Fachprojekt: Datenanalyse und Data-Mining - KDD CUP

Lukas Pfahler

(Maryam Tavakol)


Have you heard of KDD cup or Kaggle challenge? The main theme of this course is similar to this kind of competitions, however, the aim is to familiarize the students with the basics of data mining and machine learning. In this course, we study standard machine learning methods ranging from regression models to decision trees to neural networks, on a given task and dataset. We discuss how to learn various models, evaluate them, and select the best performing ones. At the end of the semester, you will be able to analyze (hopefully) any data mining task with your knowledge gained from this course.


The students work independently in small groups on the defined task that is to be solved with the help of machine learning. A seminar phase takes place periodically to give the students an overview of current methods of machine learning and data mining. Then, in groups, aspects of the problem will be worked on practically, using current techniques and tools, in order to create and evaluate a common system at the end. The results of the groups will be presented in a short final presentation as well as a written report.


Time: Wednesdays, 16:15-17:45
Language: English
Moodle: Here

First session: 15.04.2020