The explosive growth of available digital information (e.g., Web pages, emails, news, scientific literature) demands intelligent information agents that can sift through all available information and find out the most valuable and relevant information. Web search engines, such as Google, Yahoo!, and MSN, are several examples of such tools. This course studies the basic principles and practical algorithms used for information retrieval and text mining. The contents includes: 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 http://www.cs.purdue.edu/homes/lsi/CS590I_Spring_2009/Course_Description.pdf

Homepage	http://www.cs.purdue.edu/homes/lsi/CS590I_Spring_2009/Course_Description.pdf
Usually Offered:	Spring
Credit:	3 hours (class)
Prerequisite:	C/C++ programming skills (e.g., CS 240). Background in basic level of probability/statistics is preferred, but not required. Knowledge of Matlab is helpful