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Unit of study_

STAT5003: 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 methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.

Code STAT5003
Academic unit Mathematics and Statistics Academic Operations
Credit points 6
Prerequisites:
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None
Corequisites:
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None
Prohibitions:
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None
Assumed knowledge:
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STAT5002 or equivalent introductory statistics course with a statistical computing component

At the completion of this unit, you should be able to:

  • LO1. Formulate domain/context specific questions and identify appropriate statistical analysis.
  • LO2. Formulate, evaluate and interpret appropriate statistical models to describe the relationships between multiple factors.
  • LO3. Perform statistical machine learning using a given classifier and create a cross-validation scheme to calculate the prediction accuracy.
  • LO4. Understand, perform and interpret various unsupervised machine learning methods
  • LO5. Construct and implement resampling techniques to understand the behaviour of statistical models.
  • LO6. Create a reproducible report to communicate outcomes using a programming language.