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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.
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 $989.00
International Postgraduate fees
* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.
For further information see
Mathematics and Statistics.