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.
Slides and exercises will be published here.
Note:The lecture starts the lecture in the second week, i.e., on March, 9th. The exercises will then begin on March, 15th.
Introduction & Data Preprocessing
Neural Networks & Deep Learning
|20.4.||DMC Session 1|
|4.5.||DMC Session 2|
|18.5.||DMC Session 3|
|1.6.||DMC Session 4|
|8.6.||DMC Session 5|
|15.6.||DMC Session 6|
Data Mining Cup Timeline (see here):
13.04.: Task Publication
18.06.: Internal submission of reports and solutions (prequisite for taking part in the exam)
29.06.: Official submission of solutions
Note: we will be available for consulting and feedback to those who still want to tune their solutions after the exam period. On June 28th, we will select the two solutions to submit to the DMC.
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.
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