Basic principles and practical algorithms used for information retrieval and text mining: statistical characteristics of text, several important retrieval models, text categorization, recommendation system, clustering, information extraction, etc. The course emphasizes both the above applications and solid modeling techniques (e.g., probabilistic modeling) that can be extended for other applications. For more information, please see %U; http://www.cs.purdue.edu/homes/lsi/CS590I_Fall_06/CS590I.html

Homepage	http://www.cs.purdue.edu/homes/lsi/CS590I_Fall_06/CS590I.htm
Usually Offered:	Fall
Credit:	3 hours (class)
Prerequisite:	C/C++ programming skills are required (e.g., CS240). Background in basic level ofprobability/ statistics is preferred, but not required. Knowledge of Matlab is helpful
University Catalog:	CS 590I
Schedule:	Fall 2006 Instructor: Luo Si