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Detailed Course Information

 

Fall 2017
Oct 19, 2017
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Information Select the desired Level or Schedule Type to find available classes for the course.

MATH 656 - Data Mining
This course presents an introduction to computational and statistical methods for exploring large data sets and discovering patterns in them. Visualization and other exploratory methods will be used throughout the course. The course surveys methods in predictive modeling (classification) including decision trees, Naïve Bayes and nearest neighbor methods. In the process, we will study discretization, data normalization and attribute selection as well as sampling methods like cross-validation, bagging and boosting. Other topics will include cluster analysis, association analysis, anomaly detection and text mining. For all topics studied, students will work with various real and constructed data sets to see the impact of different distributions on the performance of the algorithms. A variety of performance metrics will be studied. 

The software Weka, R and Excel will be used in the course, although only basic knowledge of R and Excel will be assumed. 

Fall semester.

Text: Intro to Data Mining
Author: Tan 
ISBN: 9780321321367
Copyright Year: 2006
Publisher: Addison Wesley

Restrictions: 
Must be enrolled in one of the following Levels:      
      MN or MC Graduate 
Must be enrolled in one of the following Majors:      
      Mathematics and Statistics 

3.000 Credit hours
3.000 Lecture hours
0.000 Lab hours

Levels: MN or MC Graduate
Schedule Types: Laboratory, Lecture

Mathematics Department

Restrictions:
Must be enrolled in one of the following Levels:     
      MN or MC Graduate
Must be enrolled in one of the following Majors:     
      Mathematics and Statistics

Prerequisites:
MATH 503

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