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.
Unit details and rules
Academic unit | Mathematics and Statistics Academic Operations |
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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 |
Available to study abroad and exchange students | No |
Teaching staff
Coordinator | Justin Wishart, justin.wishart@sydney.edu.au |
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Lecturer(s) | Qiuzhuang Sun, qiuzhuang.sun@sydney.edu.au |
Justin Wishart, justin.wishart@sydney.edu.au |