- Targeted audience
- Angewandte Informatik Master with 6 credit points
- Komedia Master with 6 credit points
- ISE Master with 6 credit points
- BWL Master with 2+1 hours per week and 4 credit points :
nur Data Mining Kap. 1-7
||LB/131||Dr. Ahmet Aker|
As usual, you have to
register at the Prüfungsamt for the exams. Normally,
you have to do nothing else!
schedule your exam during the period specified above. The personal appointments for the oral
exams will be announced at our Web site on the last Tuesday
before the exam week
Only if (and only then!!!) you are not available on single days of the examination period, please send an email to our secretary Fr. Ufermann. Please observe the following guidelines:
- Do not mail us earlier than 4 weeks before, and no
later than 2 weeks before the exam period.
Most likely, exams will only take place from Monday-Thursday, so requests for Friday cannot be considered.
You should be available full-day on at least one of these days - in case you are available for a half day only, we will try our best.
In case you registered for 2 exams, both will be held together.
In case you are not at all available in the above period, we will
try to find a separate exam date for you. Only in this case,
send an email directly to
Prof. Fuhr, but not before July 1.
Emails not following the rules from above will not be answered
(like those saying 'Please give me an appointment for my exam in
...', or emails not originating from an uni-due.de mail account)
Information Mining deals with the extraction on implicit
information from raw data (Data Mining) or text (Text Mining). The
goal is the development of methods for analyzing databases and
discovering useful information by means of abstraction. For this
purpose, machine learning methods are applied.
Slides as well as sheets for the exercises can be obtained through ILIAS. For this please follow the following steps:
- Shibboleth Login -> Login with your university login information
- Scroll: Magazin -> Information Systems -> Information Mining
- Click the button "Beitreten"
The Data Mining part is based on the book
'Data Mining' by Ian Witten and Eibe Frank.
The book chapters can be
within the university network
as PDF files.
(The 2017 edition can be found
Video Lecture: Learning with Probabilities
Roberto Zicari: Big Data
- Pieters: Deep Learning for NLP (Talk slides)
- Deep learning Demos:
- SZ article on Data
Analytics (in German): Das
Erwachen, SZ vom 1.11.16
Domingos: A few useful things
to know about machine
Big Data and Data
Science. Interview with James
Kobielus, IBM Big Data
- Jürgen Cleve, Uwe Lämmel:
Data Mining. De Gruyter, 2016 (easy read, covers a subset of the
- Thomas A. Runkler; Data Mining. Vieweg+Teubner
- Trevor Hastie, Robert Tibshirani, Jerome
Elements of Statistical Learning: Data Mining,
Prediction. Springer, 2009 Series in Statistics
Shalev-Shwartz, Shai Ben-David:
Understanding Machine Learning: From
Theory to Algorithms
- Mohammed J. Zaki,
Wagner Meira: Data Mining and
Analysis: Fundamental Concepts and
Algorithms. Cambridge University