STATS 784 Statistical Data Mining

Below description edited in year: 2011

Points: 15

Coreqs: STATS 310/732, 730, 782

Prereqs: B or higher in 210. A reasonably good working knowledge of R is needed.

Credit: Final exam = 60%, test = 20%, assignments = 20%

Textbooks: (Recommended)

Hastie TJ, Tibshirani, RJ, Friedman JH, 2009.

Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd edition. Springer-Verlag.

For Advice: Thomas Yee (Email: t.yee@auckland.ac.nz | extn: 88811)

Taught: First Semester City

Website: STATS 784 website

STATS 784 will discuss the nature of data mining and a selection of topics from: what is data mining?, the classification problem, regression and decision trees, neural networks, fraud detection, data cleaning.

Disclaimer:
Although every reasonable effort is made to ensure accuracy, this information for the course year (2012), is provided as a general guide only for students and is subject to alteration. All students enrolling at the University of Auckland must consult its official document, the University of Auckland Calendar, to ensure that they are aware of and comply with all regulations, requirements and policies.