Department of Statistics
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.
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