- 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
- Medizintechnik Master with 6 credit points
|Tuesday||12:30 - 14:55||LB/131|
|Tuesday||15:00 - 15:45||LB/131||Dr. Ahmet Aker|
|5. 8. 2019||15:00 - 17:00|
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.
The Data Mining part is based on the book
'Data Mining' by Ian
Witten et al.. (accessible from within the university network only).
READ THIS BOOK!
- Other books:
Charu C. Aggarwal: Data Mining: The Textbook, Springer, May 2015
(extensive treatment of advanced application like spatial and graph data, sequences, Web, social media, and privacy issues)
- Jürgen Cleve, Uwe Lämmel: Data Mining. De Gruyter, 2016 (easy read, covers a subset of the Witten et al. book).
- Shai Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
- Mohammed J. Zaki, Wagner Meira: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014.
- Charu C. Aggarwal: Data Mining: The Textbook, Springer, May 2015 (PDF)
- Lehrangebot des Fachgebiets
- Video Lecture: Learning with Probabilities
- Roberto Zicari: Big Data
- Pieters: Deep Learning for NLP (Talk slides)
- Deep learning Demos:
- Daniel Tunkelang: 10 Things Everyone Should Know About Machine Learning
- SZ article on Data Analytics (in German): Das Erwachen, SZ vom 1.11.16
- 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.
- Chapter4-Basic Methods
- Chapter6-Trees and Rules
- Chapter7-Instance-based and Linear Models
- Gradient Descent
- Deep Learning Lecture
- Chapter8-Data Transformations
- Chapter9-Probabilistic Methods
- Chapter11-Beyond Supervised and Unsupervised Learning
- Chapter12-Ensemble Learning
- Markov Models
- Mining Sequential Patterns
- Process Mining
- Deep Learning
- Causal Inference
- Big Data