- 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
|2. 11. 20||-||Online/Moodle|
|Tuesday||15:00 - 15:45||Online/Moodle||Dr. Ahmet Aker|
|8. 3. 2021||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.
Slides and videos can be found in the Moodle course (no sign in key required)
Exercise sheets can be found in the Moodle course.
The exercises will begin on 10th of November 2020 at 3pm. The exercises will happen weekly through BigBlueButton (access through Moddle). You can use this link to participate. There are no solution submissions. However, instead during the exercise session you can present your solution and collect bonus points. To collect your bonus point you have to present at least 3 times successfully. A bonus point help to improve your exam results by 0.3, i.e. if you have 1.3 in your exam the bonus point make it to 1.0. Since we will be using the online system BigBlueButton for presentation it is advised you prepare your solution as power point and use PDF version while presenting. During the exercise I will read the question. You can raise your interest to present your solution. If there are more than one student interested in presenting there will be a selection through a number guess. The selected student can then upload his/her solution for presentation. The first exercise sheet will be uploaded on the 3rd of November and discussed on the 10th of November 2020.
IM Klausurergebnisse und Klausureinsicht
Die Klausureinsicht findet am 23.10.2021 um 15Uhr im Raum LF 137 statt. Bitte mit Maske kommen und 3 G Regel beachten. Bitte unbedingt Dr. Aker kontaktieren, um einen Termin zu vereinbaren. IM exam inspection will be on the 23.10.2021 at 3pm in LF 137. Please come with a mask and obey the 3 G rule. Please contact first Dr. Aker to make an appointment.
(Der Zugriff ist nur mit einer IP-Adresse der Universität (Proxy, WLAN oder VPN) möglich.)