Statistical machine learning deals with the problem of finding a predictive function based on data, and focuses on the intersection of statistics and machine learning. It involves the development of algorithms that learn from observed data by constructing stochastic models, which can be used for making predictions and decisions. In this module, students study statistical machine learning methods and how they are implemented in R. Both theoretical and practical aspects are covered.
Indicative content: Classification and prediction; K-Nearest Neighbours; cross-validation and bootstrap; classification and regression trees; bagging; random forests; boosting; support vector classifiers; support vector machine (SVM); regression SVM; semisupervised learning.
Total contact hours: 44
Private study hours: 106
Total study hours: 150
Assessment 1 (10-15 hrs) 20%
Assessment 2 (10-15 hrs) 20%
Examination (2 hours) 60%
Reassessment methods
Like-for-like
Bishop, C. M. (2006), Pattern Recognition and Machine Learning. Springer, New York
James, G, Witten, D., Hastie, T., Tibshirani, R. (2013) Introduction to Statistical Learning. Springer, New York.
Brett, L. (2019) Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition. Packt Publishing, Birmingham.
Berry, M., and Linoff, G. (2012). Data Mining Techniques: For Marketing, Sales and Customer Relationship Management.
1.demonstrate systematic understanding of key aspects of machine learning;
2.demonstrate the capability to deploy established approaches accurately to analyse and solve problems using a reasonable level of skill in calculation and manipulation of the material in the following areas: Supervised learning and prediction, regression tree-based methods and support vector machines;
3.apply key aspects of machine learning in well-defined contexts, showing judgement in the selection and application of tools and techniques;
4.show judgement in the selection and application of machine learning approaches.
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