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Unit of study_

Computational Statistical Methods - STAT5003

Year - 2019

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.

Classes
2x1-hr lectures; 1x1-hr tutorial/wk

Assessment
Assignments (40%), quizzes (20%); 2-hour final examination (40%)

Textbooks
(1) An Introduction to Statistical Learning (with Applications in R), Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, (2014), Springer; (2) Computational Statistics, Geof H. Givens, Jennifer A. Hoeting, Wiley (2005); (3) Applied Predictive Modeling, Max Kuhn, Kjell Johnson, (2013), Springer; (4) Introductory Statistics with R, Peter Dalgaard, (2008), Springer.

Pre-requisites

STAT5002

Details

Faculty: Science

Semester 1

25 Feb 2019

Department/School: Mathematics and Statistics Academic Operations
Study Mode: Normal (lecture/lab/tutorial) evening
Census Date: 31 Mar 2019
Unit of study level: Postgraduate
Credit points: 6.0
EFTSL: 0.125
Available for study abroad and exchange: No
Faculty/department permission required? Yes
Location
Camperdown
Faculty: Science

Semester 2

05 Aug 2019

Department/School: Mathematics and Statistics Academic Operations
Study Mode: Normal (lecture/lab/tutorial) evening
Census Date: 31 Aug 2019
Unit of study level: Postgraduate
Credit points: 6.0
EFTSL: 0.125
Available for study abroad and exchange: No
Faculty/department permission required? Yes
Location
Camperdown
More details
Unit of Study coordinator: A/Prof Shelton Peiris
HECS Band: 2
Courses that offer this unit

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