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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 research group "Web Science" (Institute AIFB; Prof. York Sure-Vetter).

The aim of the Seminar "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) design and development of the system; (2) actual practical implementation (software engineering & development); (3) the final presentation; (4) the written report (Seminararbeit), which should also contain the necessary theoretical foundations for explaining the software project and the implemented system.

Note that this seminar focuses on the design and implementation of a research prototype system. Thus, all participating students should have good programming skills (backend and/or frontend) and some experience in data processing. Please indicate in the motivation letter when applying for this seminar, which skills you can bring in and extend in the frame of the seminar's project.

At the first meeting at the start of the semester, a selection of projects will be presented, together with an overview of the tasks to be solved and the data sets which can be used. 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. One can imagine topics like

  • building a recommender system which can recommend which publications to read and to cite;
  • building a recommender system which can recommend which machine learning approach to use and why;
  • extracting information from texts and modeling it semantically for a semantic search system;
  • building a knowledge graph for product recommendation;
  • automatically determining the bias of news articles;
  • automatically determining based on news articles which city is affected by the coronavirus;
  • ...

All students will be given the chance to write a scientific publication together with the supervisor based on the project's outcomes (i.e., seminar report). In this way, students will gain international visibility in the area of data science and machine learning, which might be beneficial for future applications and career paths. We particularly encourage female students to apply for this seminar.

The kick-off event will take place on April 27, 2020 (to be confirmed!).

Interested? Then apply here:


Literature

Relevant literature will be given after project assignment.