Information Mining
2016-10-01

Teaching personnel


Lecturer
Tutor(s)

Formalia


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

Dates


Lectures

Date

Time

Start

Place

Tuesday 12:30 - 14:55 18.10. LB/131

Tutorials

Date

Time

Start

Place

Tutor

Tuesday 15:00 - 15:45 25.10. LB/131Dr. Ahmet Aker

Examination Dates


Oral Exam

PeriodPlace
27.03.2017 - 31.03.2017LE/313

As usual, you have to register at the Prüfungsamt for the exams. Normally, you have to do nothing else!

We will 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)

 

Description


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.

Lecture material


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"

Additional material