Information Mining
Teaching personnel
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-Teil
Dates
Lectures
Date | Time | Start | Place |
| Dienstag | 12:30 - 14:55 | LB/131 |
Tutorials
Date | Time | Start | Place | Tutor |
| Dienstag | 15:00 - 15:45 | LB/131 | Michael Rist, M.Sc. |
Examination Dates
Oral Exam
| Period | Place |
|---|---|
| 7.03.2016 - 10.03.2016 | LE/313 |
As usual, you have to
register at the Prüfungsamt for the exams, and then we will
schedule your exam. The personal appointments for the oral
exams will be announced at our Web site after the end of the
withdrawal period on March 1.
If (and only if!!!) 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 the exam date, and no later than February 22.
- Most likely, exams will only take place from Tuesday-Thursday, so requests for Monday or Friday cannot be considered.
- You should be available full-day on at least one of these three 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 January 15.
Example exam questions
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 pupose, machine learning methods are applied.
Lecture material
- The Data Mining part is based on the book 'Data Mining' by Ian Witten and Eibe Frank. The book chapters can be accessed/downloaded from the university network as PDF files. .
-
Clustering Tutorial
Clustering Demo - Video Lecture: Learning with Probabilities
- Roberto Zicari: Big Data
- Pieters: Deep Learning for NLP (Talk slides)
- Deep learning Demos:
-
Further reading:
- Pedro Domingos: A few useful things to know about machine learning
- On Big Data and Data Science. Interview with James Kobielus, IBM Big Data Evangelist.
- Thomas A. Runkler; Data Mining. Vieweg+Teubner 2009
- Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009 Series in Statistics
- Shai Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning: From Theory to Algorithms
Slides
For copyright reasons, some chapters are accessible from the university network only!
Introduction to IM: pdf
-
Data Mining
- Chapter 1: Introduction odp pdf
- Chapter 2: Imput: Concepts, Instances, Attributes odp pdf
- Chapter 3: Output: Knowledge Representation odp pdf
- Chapter 4: Algoriths: The Basic Methods odp pdf
- Chapter 5: Credibility: Evaluating what's been learned odp pdf
- Chapter 6: Implementation: Real machine learning schemes odp pdf
- Chapter 7: Data transformations odp pdf
- Chapter 8: Ensemble learning odp pdf
- Random Forests (not relevant for exam) pdf
- Data Warehouse: (not relevant for exam) odp pdf
- Big Data: (Lokal) (Slideshare)
- Wiederholung
- Mining Time Series and Sequences
- Process Mining
- Graph Mining
- Deep learning (not relevant for exam) pdf
Exercises
- Exercise sheet 1
- Exercise sheet 2
- Exercise sheet 3
- Exercise sheet 4
- Exercise sheet 5
- Exercise sheet 6
- Exercise sheet 7
- Exercise sheet 8
- Exercise sheet 9
- Exercise sheet 10
- Exercise sheet 11
- Exercise sheet 12
- Exercise sheet 13
Some useful links for RapidMiner
- Download RapidMiner at Rapidminer.com
- RapidMiner's Documentation
- News item (in german):Datamining: RapidMiner liegt vorn