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STAT318 and STAT462 are courses in statistical learning and data mining, suited to anyone with an interest in analysing large datasets. The courses will introduce a variety of statistical learning and data mining techniques for classification, regression, clustering and association purposes. Possible topics include, classification and regression trees, random forests, Apriori algorithm, FP-growth algorithm and support vector machines. The lectures will be supplemented with laboratory sessions using the statistical software package R.
The courses will:introduce statistical learning and data miningintroduce advanced data analysis techniques for classification, regression, cluster analysis and association analysisintroduce the use of the statistics software package RYou will be able to:describe and conduct appropriate statistical modeling techniquesbe able to interpret the analysis results in such a way that a non-user of statistics can understandUse R competentlyWrite a scientific and technical report
Subject to approval of the Head of School.
Students must attend one activity from each section.
Giulio Dalla Riva
Hastie, Trevor. , Tibshirani, Robert., Friedman, J. H;
The elements of statistical learning : data mining, inference, and prediction
An introduction to statistical learning : with applications in R
G. James, D. Witten, T. Hastie and R. Tibshirani, An Introduction to Statistical Learning with Applications in R. (2014) SpringerRecommended reading:T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction. (2013) Springer.
School of Mathematics and Statistics Postgraduate Handbook
General information for students
Domestic fee $1,017.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics