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.
2x1-hr lectures; 1x1-hr tutorial/wk
Assignments (40%), quizzes (20%); 2-hour final examination (40%)
(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.