Building on the Data Mining fundamentals course, this course deepens the theory and practice of advanced data mining topics, such as:
The course consists of a lecture together with accompanying practical exercises as well as student team projects. In the exercises the participants will gather initial expertise in applying state of the art data mining tools on realistic data sets.
Like in the previous years, participants will take part in the annual Data Mining Cup (DMC), an international student competition in data mining, as part of the project work. In addition to the DMC submission, the approaches and results of the project have to be compiled into a written project report, and presented in a plenary session.
We'll have two alternatives for the exercise:
Both of these dates are offered, and you have to decide for one.
Unlike in the previous years (and unlike, e.g., Data Mining 1), the project is not graded. Your final grade will be based solely and entirely on the final exam.
Note:The lecture starts the lecture in the second week, i.e., on February, 18th. The exercises will then begin on February, 24th.
Introduction & Data Preprocessing
Neural Networks & Deep Learning
|24.3.||DMC Session 1|
|31.3.||DMC Session 2|
|7.4.||Easter Break, Work on DMC Task|
|14.4.||Easter Break, Work on DMC Task|
|21.4.||DMC Session 3|
Data Mining Cup Timeline (see here):
Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.
Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.
Bing Liu: Web Data Mining, 2nd Edition, Springer.
Further literature on specific topics will be announced in the lecture.
Tracking cookies are currently allowed.
Tracking cookies are currently not allowed.