Data Mining II (FSS 2024)

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, students enrolled in the course will participate in a larger data mining competition (details to be announced). In addition to the submission of an entry to the competition, 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, A5, 6, C013 (starts on February 13th!)

We'll have two alternatives for the exercise:

  • Exercise: Monday, 12.00 – 13.30, A5, 6, C013
  • Exercise: Monday, 13.45 – 15.15, A5, 6, C013

Both exercises are equivalent, you are supposed to attend one.

  • Lecture Slides

    Lecture slides will be made available here as the course progresses.

  • 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, places will be allocated automatically by Portal2.
  • Outline

    WeekExercise (Monday)Lecture (Tuesday)
    12.2.--Introduction & Data Preprocessing
    19.2.Introduction & Data Preprocessing--
    4.3.EnsemblesTime Series
    11.3.Time Series--


    Neural Networks & Deep Learning


    Easter Break


    Easter Break
    8.4.Neural Networks & Deep LearningAnomaly Detection
    15.4.Anomaly DetectionHyperparameter Tuning

    Hyperparameter Tuning

    Model Verification
    29.4.Model VerificationChallenge Session
    6.5.--Challenge Session
    13.5.--Challenge Session
    20.5.--Challenge Session
  • 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).