| SSC 358/CS 363D, Spring 2014 |
| GDC 1.304, Mon & Wed 3:30 - 5:00 pm |
| Home | | | Instructor | | | Syllabus | | | Homeworks | | | Projects |
| Overview |
In recent years, rapid developments in data collection and storage technologies have led to data sets that are "big" in many senses of the word. Data mining is the automatic discovery of interesting patterns and relationships in such "big data". This undergraduate course will provide an introduction to the topic of data mining, and some statistical principles underlying its key methods. Topics covered will include data preprocessing, regression, classification, clustering, dimensionality reduction, and association analysis.
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| Grading |
5 Homeworks (25%) 1 Midterm (16%) + 1 Final (24%) Final Project (30%): Initial Project Milestone (5%) due Apr 02; Final Project Report (25%) due May 02 Class Attendance and Participation (5%) |
| Textbooks |
Introduction to Data Mining. P. Tan, M. Steinbach, V. Kumar, Addison Wesley, 2006.
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| Piazza |
All class materials and announcements will be on the class Piazza site. If you are registered for the course, you should have received an email with instructions on accessing the Piazza site. If not, please email me or the TA.
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