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:
The exercises start on February 27th.
All exercises are equivalent, you are supposed to attend one.
|21.2.||Introduction & Data Preprocessing||--|
|28.2.||Ensembles||Introduction & Data Preprocessing|
|14.3.||Neural Networks & Deep Learning||Time Series|
|21.3.||New: KDD Cup Kick Off|
Neural Networks & Deep Learning
|11.4.||Easter Break||Easter Break|
|9.5.||Anomaly Detection||Model Verification|
|16.5.||KDD Cup||Anomaly Detection|
KDD 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.