STATS 784 Statistical Data Mining

Below description edited in year: 2018

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: (Suggested background reading)

James G., Witten D., Hastie T., Tibshirani R., 2013.

An Introduction to Statistical Learning with Applications in R Springer: NY, USA.

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

Taught: Second 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? [includes big-Oh notation, regularization and fraud detection], efficient R programming, graphical methods for big data, regression and decision trees, the classification problem. Possibly vector generalized linear and additive models will be intertwined with the above.

Disclaimer:
Although every reasonable effort is made to ensure accuracy, this information for the course year (2018), 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.