杏吧论坛

Dr Xinghao Qiao

This course is compulsory on the MSc in Data Science. This course is available on the MSc in Applied Social Data Science, MSc in Marketing, MSc in Quantitative Methods for Risk Management, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

The course will be taught from a statistical perspective and students must have a solid understanding of linear regression models

Students are not permitted to take this course alongside Algorithmic Techniques for Data Mining (MA429)

Machine learning and data mining are emerging fields between statistics and computer science which focus on the statistical objectives of prediction, classification and clustering and are particularly orientated to contexts where datasets are large, the so-called world of 'big data'. This course will start from the classical statistical methodology of linear regression and then build on this framework to provide an introduction to machine learning and data mining methods from a statistical perspective. Thus, machine learning will be conceived of as 'statistical learning', following the titles of the books in the essential reading list.   The course will aim to cover modern non-linear methods such as spline methods, generalised additive models, decision trees, random forests, bagging, boosting and support vector machines, as well as more advanced linear approaches, such as ridge regression, the lasso, linear discriminant analysis, k-means clustering, nearest neighbours. 

20 hours of lectures and 10 hours of computer workshops in the MT.

The first part of the course reviews regression methods and covers linear and quadratic discriminant analysis, cross-validation, variable selection, nearest neighbours, shrinkage, dimension reduction methods. The second part of the course introduces non-linear models and covers, splines, generalized additive models, tree methods, bagging, random forest, support vector machines, principal components analysis, k-means, hierarchical clustering.

Week 6 will be used as a reading week.

Students will be expected to produce 5 problem sets and 1 project in the MT.

The problem sets will consist of some theory questions and data problems that require the implementation of different methods in class using a computer package.

James, G., Witten, D., Hastie, T. and Tibshirani, R. An Introduction to Statistical Learning. Springer, 2014. Available online at http://www-bcf.usc.edu/~gareth/ISL/  

Hastie, T., Tibshirani, R. and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd Edition, Springer,  2009. Available online at http://statweb.stanford.edu/~tibs/ElemStatLearn/index.html 

Bishop, G. Pattern Recognition and Machine Learning. Springer-Verlag, 2006.

Exam (70%, duration: 2 hours) in the summer exam period.
Project (30%) in the MT Week 11.