Photo credit: Anna Logue

Data Mining II (FSS 2020)

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


  • Tuesday, 13.45 - 15.15,  B6 A1.01

We'll have two alternatives for the exercise:

  • Exercise: Monday, 10.15 - 11.45, A 5, 6, C012
  • Exercise: Monday, 12.00 - 13.30, A5, 6, C015

Both of these dates are offered, and you have to decide for one.

Final exam

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.

  • Slides and Excercises

  • 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 February, 18th. The exercises will then begin on February, 24th.

    Date Topic

    Introduction & Data Preprocessing

    25.2. Ensembles
    3.3. Time Series

    Neural Networks & Deep Learning


    Hyperparameter Tuning

    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

    Anomaly Detection


    Model Verification

     Data Mining Cup Timeline (see here):

    • March 19th: Task Announcement
    • April 23rd: Submission of Results
  • 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).