Courses / Module

STATISTICAL MACHINE LEARNING

Module code: ST644
Credits: 5
Semester: 2
Department: MATHEMATICS AND STATISTICS
International:

	Overview
	Module Objective: To develop the students knowledge of Statistical learning techniques. Introduction to statistical learning. Classification: logistic regression, LDA and QDA. Unsupervised learning: PCA, clustering methods. Resampling methods: cross-validation and the bootstrap. Linear Model selection and regularisation. Modelling with regression splines and smoothing splines. Tree-based methods. Support vector machines. Implementation in R. ST644 is equivalent to ST404 (i.e. lectures and tutorials as for ST404).

Overview

Module Objective:
To develop the students knowledge of Statistical learning techniques.

Introduction to statistical learning. Classification: logistic regression, LDA and QDA. Unsupervised learning: PCA, clustering methods. Resampling methods: cross-validation and the bootstrap. Linear Model selection and regularisation. Modelling with regression splines and smoothing splines. Tree-based methods. Support vector machines. Implementation in R.

ST644 is equivalent to ST404 (i.e. lectures and tutorials as for ST404).

	Learning Outcomes
	On successful completion of the module, students should be able to: Construct and apply statistical learning models including classification models, generalised additive models, tree-based models and support vector machines. Apply and interpret principal component analysis and clustering algorithms.

Teaching & Learning methods

Assessment

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? No

	Pre-Requisites
	ST633 or equivalent

Timetable

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Courses / Module

STATISTICAL MACHINE LEARNING

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Semester 2