Advanced Topics in Knowledge Management and Discovery KMD




DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Oberseminar (OS) - Senior Seminar - Dates/Times/Location:
Mon. 11:00 bis 13:00 weekly G29-021 Hielscher ,
Spiliopoulou Lehrpreisträger/-in



Overview (from LSF)

Learning Content

Each semester, this seminar has different topics for the scientific papers. Topic areas include:
- Stream mining and timeseries analysis
- Recommenders and opinion mining
- Medical mining
- Active and semisupervised learning

Each seminar assignment is on a specific topic. The assignment

(1) collection of papers in the topic's area,

(2) selection of up to 6 papers,

(3) reading the papers and grouping them thematically,

(4) evaluating them on different scientific criteria and commenting on them.

The seminar assignment is a homework that takes the form of a report. The report contains subreports on each of the 4 tasks above, as well as
intermediate presentations (not necessarily with slides) and a final presentation, the slides of which also become part of the homework report.

All meetings of the seminar are mandatory.


The topics of the seminar are presented in the first or second week of each semester. The exact date and time is announced at the KMD website
and in print, short before the semester start. Students apply for topics after the presentation. The assignments are done ca. one week after the
topic presentation.

The timeline, including milestones for the different tasks, and dates for the presentations will also be presented together with the topics.
All meetings are mandatory.


In this seminar, you learn to identify scientific papers on a specific topic, then read, comment and compare these papers, group them
thematically and rank them on different criteria. The topic areas change in each semester, but are al the domain "Knowledge Management and


Background in data mining or machine learning is needed for this seminar

Target Group

Students of master degrees as listed under "Studiengänge", with interest in data science and background in machine learning / data mining / artificial intelligence.


Topics and Timeline for Winter term 2018/2019  here


Last Modification: 16.10.2018 - Contact Person:

Sie können eine Nachricht versenden an: Prof. Dr. rer. nat. habil. Myra Spiliopoulou