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Lehre/Praktikum Knowledge Discovery and Data Science/en

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Knowledge Discovery and Data Science



Details of Course
Type of course practical course
Lecturer(s) York Sure-Vetter
Instructor(s) Michael Färber, Anna Nguyen
Subject Maschinelles Lernen, Künstliche Intelligenz, Data Science
Credit Points
Control of Success
Term summer


You find additional information, the time schedule and room numbers in the University Course Overview.

Course Overview http://ilias.studium.kit.edu
Student Portal https://campus.studium.kit.edu



Research Group


Content

This seminar will be given in English and will be provided by the group of Prof. York Sure-Vetter.

The aim of the Seminar/Praktikum Knowledge Discovery and Data Science is the implementation of a data science project. This includes the data preparation, modeling, computation, and scientific evaluation of the developed system.

The following aspects will be taken into consideration for the grade: (1) the practical implementation (software development); (2) the final presentation; (3) the written report, which should also contain theoretical basics for the corresponding data mining area.

At the first meeting (at the start of the semester), a selection of projects (with descriptions of the tasks and data sets to be used) will be presented. Then, groups of 2-3 people will be formed, and each group will work on one project.

Potential topics are located in the field of Data Science, Machine Learning, Natural Language Processing, and Semantic Web. Participants can adapt the proposed tasks and topics if desired.


Literature

Detailed references will be given along with each topic. Some fundamental text books are:

  • Mitchell, T.; Machine Learning, McGraw Hill, 1997.
  • Cook, D.J. and Holder, L.B. (Editors) Mining Graph Data, ISBN: 0-471-73190-0, Wiley,
  • Manning, C. and Schütze, H.; Foundations of Statistical NLP, MIT Press, 1999.