| Data Mining - 58713 - CS 57300 - LE1 |
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Associated Term: Fall 2013
Levels: Undergraduate, Graduate, Professional West Lafayette Campus Lecture Schedule Type Learning Outcomes: Data Mining has emerged at the confluence of artificial intelligence, statistics, and databases as a technique for automatically discovering summary knowledge in large datasets. This course introduces students to the process and main techniques in data mining, including classification, clustering, and pattern mining approaches. Data mining systems and applications will also be covered, along with selected topics in current research. More details may be found at http://www.cs.purdue.edu/homes/neville/courses/CS573.html . While this is for a prior offering of the course, but this Fall 2013 offering will have basically the same coverage and requirements (although the projects will make use of a newly released real-world dataset rather than the one used in 2012.) Required Materials: D. Hand, H. Mannila, P. Smyth (2001). Principles of Data Mining. MIT Press. Technical Requirements: Note that the prerequisites are for CS38100 or an equivalent undergraduate level algorithms course. This requires data structures and two semesters of programming as a prerequisite; there are significant programming assignments as part of CS57300. There is also a statistics requirement - while Stat 51600 is the official prerequisite, doing well in Stat 41600 or Stat 35000 will generally be sufficient to get permission of the instructor. View Catalog Entry
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