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.
At the moment, we assume that the course can be held in presence, but we are closely monitoring the pandemic situation, and we are prepared to switch to an online or hybrid setting.
For students who cannot attend the lecture (e.g., due to visa problems or quarantine), we will provide lecture recordings from the previous year.
We'll have three alternatives for the exercise:
The exercises start on February 28th.
All exercises are equivalent, you are supposed to attend one out of the three.
|21.2.||Introduction & Data Preprocessing||--|
|28.2.||Ensembles||Introduction & Data Preprocessing|
|14.3.||Neural Networks & Deep Learning||Time Series|
Neural Networks & Deep Learning
|11.4.||Easter Break||Easter Break|
|18.4.||Easter Break||Easter Break|
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.