Photo credit: Anna Logue

Data Mining II

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

  • Data Preprocessing
  • Regression and Forecasting
  • Dimensionality Reduction
  • Anomaly Detection
  • Time Series Analysis
  • Parameter Tuning
  • Ensemble Methods
  • Deep Learning

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

Lecture:

  • Tuesday, 13.45 - 15.15,  EO 145 (Schloss Ehrenhof Ost / castle) <- changed!

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.

The exam review for the first exam from FSS2019 will take place on Monday, August 19th, at 8am in B6 C1.01.

The exam review for the second exam from FSS2019 will take place on Thursday, September 19th, at 10am in B6 C1.01.

  • Slides and Excercises

    Slides

    Exercises

  • 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:Since the introduction to team projects takes place on February 12th during the lecture slot, and some of you may want to attend, we'll start the lecture in the second week, i.e., February, 18th. The exercises will then begin on February, 25th.
     

    Week Topic
    11.2.

    No Lecture (due to introduction of team projects)

    18.2.

    Organization, Preprocessing

    25.2. Regression
    4.3.

    Anomaly Detection

    11.3.

    Ensemble Methods

    18.3. Neural Networks
    25.3. Time Series
    1.4.

    Parameter Tuning

    8.4.

    DMC intermediate presentation

    15.4. Easter Break
    22.4. Easter Break
    29.4.

    DMC intermediate presentation

    6.5.

    DMC intermediate presentation

    13.5.

    DMC final selection

    Timeline Data Mining Cup:

    • Team registration: from March 5, 2019
    • Task is announced: April 4, 2019
    • Deadline for submissions: May 16, 2019
    • Presentation & award ceremony: July 3, 2019
  • 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).