Advanced Topics in Knowledge Management and Discovery


DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Seminar - Seminar - Dates/Times/Location:
Mon. 13:00 bis 15:00 weekly   Hielscher ,

Overview (from LSF)

Learning Content


In this master seminar, advanced topics in knowledge management and discovery (data mining, machine learning, ...) will be presented and discussed.

  • Adaptive Classification (Concept/Population Drift, Change Mining, Semi-Supervised and Active Learning, Learner-Induced Drift, Learning under Latency, Adversarial Machine Learning)
  • Adaptive Clustering
  • Sentiment/Opinion Mining (Sentiment Classification, Feature-based Opinion Mining, Sentiment Lexicons, Sentiment Summarization)
  • Recommendation Systems
  • Mining in Health Care / Neurobiological Data


What you will learn

Each participant has to pick a topic out of a pool of topic from their respective supervisor. The topics encompass one till two research papers. Based on the paper(s) the student has to pick a third one by his own. To guide the student while reading the papers, we are going to provide a list of questions which have to be answered for each paper. That lists have to be submitted by the review due (cf. timline). Feedback towards the reviews is provided by us afterwards. According to the feedback you start writing a small survey (3 pages) of the read papers. Each student has to give a presentation of 15-20 minutes where he/she compares the papers and also explaines the main contributions of the papers.



Seminar presentation slots are scheduled for Monday 27th of June in G29-021 from 9:15 to 10:50. Presentations are 15 minutes, Q&A 5 minutes and 5 minute breaks between each slot.




Seminar topics will be assigned by the supervisors. Please contact Tommy Hielscher for further information.

Topics for the summer term 2016 will be presented by the KMD group on 11.04.2016 at 3:15 p.m. in the room G22A-110 (see the announcement).


Recent publications on advanced topics in knowledge management and discovery (data mining, machine learning,  ...)

Target Group

Master students in informatics or in related studies who have already attended and presented at a (bachelor) seminar.

Course Material



Last Modification: 17.10.2017 - Contact Person: