- 2017-09-01: more information will follow soon
- Core course, 9 ECTS credits
- Anna Christina Guimaraes
- Alexander Marx
- Azin Ghazimatin
- Cuong Xuan Chu
- Janis Kalofolias
- Kailash Budhathoki
- Kashyap Popat
- Preethi Lahoti
- Sebastian Dalleiger
Time & Location
The first lecture is on Wednesday, October 18, 2017.
- Monday, 14-16
- Tuesday, 10-12
First tutorials will be on October 23 and October 24, 2017. There will be several tutorial groups at these times. More details will be announced soon.
Information Retrieval (IR) and Data Mining (DM) are methodologies for organizing, searching and analyzing digital contents from the web, social media and enterprises as well as multivariate datasets in these contexts. IR models and algorithms include text indexing, query processing, search result ranking, and information extraction for semantic search. DM models and algorithms include pattern mining, rule mining, classification and recommendation. Both fields build on mathematical foundations from the areas of linear algebra, graph theory, and probability and statistics.
Good knowledge of undergraduate mathematics (linear algebra, probability theory) and basic algorithms.
We will use the following primary textbooks.
For Probability and Statistics,
- Larry Wasserman: All of Statistics, Springer, 2004
For Data Mining,
- Charu Aggarwal: Data Mining - The Textbook, Springer, 2015
For Information Retrieval,
- Chris Manning, Prabhakar Raghavan, Hinrich Schütze: Introduction to Information Retrieval, Cambridge, 2008
- ChengXiang Zhai, Sean Massung: Text Data Management and Analytics, Morgan Claypool, 2016
We will list other interesting and potentially useful books later.