Data Mining II (FSS 2021)

Building on the Data Mining fundamentals course, this course deepens the theory and practice of advanced data mining topics, such as:

  • Data Preprocessing
  • Dimensionality Reduction
  • Anomaly Detection
  • Time Series Analysis and Forecasting
  • Parameter Tuning
  • Ensemble Methods
  • Neural Networks and Deep Learning
  • Model Validation

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.

Time and Location

Until further notice, the lecture and exercise are conducted fully virtually.


We'll have two alternatives for the exercise:


  • Lecture Slides

  • Participation

    • The course is open to students of the Master Business Informatics, Master Data Science, and Lehr­amt Informatik.
    • Registration is done via Portal2.
    • In case there are more registration than places (64), places will be allocated automatically by Portal2.
  • Outline

    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

    23.3.Time Series
    30.3.Easter Break
    6.4.Easter Break

    Neural Networks & Deep Learning

    20.4.DMC Session 1

    Hyperparameter Tuning

    4.5.DMC Session 2

    Anomaly Detection

    18.5.DMC Session 3

    Model Verification

    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.

  • Literature

    1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.

    2. Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.

    3. Bing Liu: Web Data Mining, 2nd Edition, Springer.

    Further literature on specific topics will be announced in the lecture.

  • Software

    • We will use Python with a number of different packages (scikit-learn, etc.), which will be announced during the exercises.
    • You are invited to work with other tools (RapidMiner, R, etc.) if you like.
  • Lecture Videos

    • Video recordings of the Data Mining II lectures are available here (accessible from within the university network or VPN).