Core Lecture "Information Retrieval and Data Mining" WS 2007/08
Lecturer: Prof. Dr.-Ing. Gerhard Weikum
The lecture teaches mathematical models and
algorithms that form the basis for search engines for the Web,
intranets, and digital libraries and for data mining and analysis
tools. Information Retrieval and Data Mining are technologies for
searching, analyzing and automatically organizing text documents,
multi-media documents, and structured or semistructured data.
Students planning to attend the course should be familiar with basic
models and methods from linear algebra (e.g. singular-value
decomposition) as well as probability theory and statistics (e.g.
Bayesian networks and Markov chains).
Requirements for Passing the Course and Grading System:
- The lecture slides will be made available here
during the course of the semester.
- The assignment sheets will be made available here during the course of the semester.
- Lecture: Tuesday and Thursday, 14:00 - 16:00, in building E1.3,
14:00-16:00, in building E1.4, room 023
Friday, 16:00-18:00, in building E1.4, room 023
- Results of the first quick test
- Results of the second quick test
- Results of the third quick test
- Final grades online!
To pick up your 'Schein', please come to Ms. Schaaf (room 402) starting March 19, 2008.
- Webmaster: Please contact Gerard de Melo
in case of problems pertaining to the website.
- Soumen Chakrabarti. Mining the Web, Morgan Kaufmann,
2002, 2nd Edition in Preparation. (Website)
- Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze.
Introduction to Information Retrieval, Cambridge University
Press, 2008. (Website)
- Jiawei Han, Micheline Kamber. Data Mining - Concepts and
Techniques, Morgan Kaufmann, 2000. (Website)
- Background on Statistics:
Larry Wasserman. All of Statistics, Springer, 2004. (Website)
- Background on Machine Learning:
Richard O. Duda, Peter E. Hart, David G. Stork. Pattern
Classification, 2nd Edition, Wiley & Sons, 2001. (Website)