Seminar Data-Science I (Methods)

CS 721 Master Seminar (M. Sc. Wirt. Inf., M.Sc. MMDS, Lehr­amt für Gymnasien)

LecturerProf. Dr. Markus Strohmaier, Marlene Lutz, Tobias Schumacher
Course FormatSeminar
Credit Points4 ECTS
GradingWritten report with oral presentations 
Examination dateSee schedule below
Information for Students

The course is limited to 15 participants. Please register centrally via Portal2.


For administrative questions, please contact

Marlene Lutz, M.Sc.

Marlene Lutz, M.Sc.

For further information please contact Marlene Lutz.

Course Information

  • Course Description

    In this seminar, students perform scientific research, either in the form of a literature review or by conducting a small experiment, or a mixture of both, and prepare a written report about the results. Topics of interest focus around a variety of problems and tasks from the fields of Data-Science, Network Science and Text Mining. 

    Previous participation in the courses “Network Science” and “Text Analytics” are recommended.

  • Objectives

    Expertise: Students will acquire a deep understanding of the research topic. He/she is expected to describe in-depth and summarize the topic in detail in his/her own words, as well as to judge the contribution of the research papers to ongoing research.

    Methodological competence: Students will develop methods and skills to find relevant literature for his/her topic, to prepare methodologically sound scientific experiments, and to write a well-structured scientific paper and to present his/her results. He/she will be also aware of the need to avoid plagiarism.  The key qualification Scientific Research is highly recommended as a prerequisite for the seminar.

    Personal qualification: Students will acquire skills on how to find relevant literature for a research topic, organize a small research task, write a well-structured, concise paper about it and present the results of their work. He/she is well prepared to write and present a Master’s Thesis. 

  • Topics

    Deep learning methods have emerged as key tools to represent and infer on data. Yet, there is no one-fits-all solution, and different kinds of data require different modeling approaches to properly meet their characteristics.

    In this seminar, we consider neural network models for language and network data, and specific challenges that arise when modelling these kinds of data. Thus, this seminar is split into two blocks: the first examines the responsible use of language models, whereas the second block explores methods for fair network representation learning. In both blocks, we will consider recent studies from top-tier conferences, aiming to gain deep understanding of state-of-the-art approaches for the given deep learning problems.

  • Schedule

    The schedule below is preliminary, dates are subject to change.

    Registration period

    until 12.02.2024via Portal2
    Kick-off meeting

    22.02.2024, 10:00

    General information

    Drop-out until25.02.2024, 23:59 
    Midterm22.03.2024, 08:30 – 13:30Presentations
    Endterm12.04.2024, 08:30 – 13:30Presentations
    Submission deadline

    05.05.2024, 23:59

    Written report
  • Registration

    Please register via Portal2.