- 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||13:20 - 15:45||LB/131|
|Tuesday||12:30 - 13:15||LB/131||Dr. Ahmet Aker|
|16. 3. 2020||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 major part of the course
is based on the book
'Data Mining' by Ian
Witten et al..
(There is also 4th edition, but the course presents the new material in a different way).
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
- Roberto Zicari: Big Data
- Pieters: Deep Learning for NLP (Talk slides)
- Deep learning Demos:
- François Chollet: On the Measure of Intelligence
- 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
- Gradient Descent
- Deep Learning 1
- Deep Learning 2
- Chapter6-Trees and Rules
- Chapter7-Instance-based and Linear Models
- Deep Learning 3
- Deep Learning 4
- Chapter8-Data Transformations
- Chapter9-Probabilistic Methods
- Chapter11-Beyond Supervised and Unsupervised Learning
- Chapter12-Ensemble Learning
- Markov Models
- Mining Sequential Patterns
- Process Mining
- Causal Inference