Computational Statistical Methods
The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation, simulation methods, sampling methods, the Bootstrap, Monte Carlo, Cross Validation and Jackknife approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice. Finally, this unit will show how to make inferences about populations of interest in data mining problems.
See also the STAT5003 information in the University's unit of study database.
Last revised 07/08/18
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