- Department: Mathematics
- Module co-ordinator: Prof. Julie Wilson
- Credit value: 10 credits
- Credit level: H
- Academic year of delivery: 2022-23
- See module specification for other years: 2021-22
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
- None
Occurrence | Teaching period |
---|---|
A | Spring Term 2022-23 |
This module builds on the concepts introduced in Statistical Pattern Recognition and provides students with the ability to implement statistical and machine learning methods. The aim is to allow students to perform statistical data analyses of real data, from the thorough formulation of the question to be investigated up to the presentation of the analysis' results.
It is a practical and project-oriented module with an overview of a range of methods, followed by their use in the statistical software environment R.
Students will complete three assessed exercises during the term and carry out a data analysis project in an open assessment over 2 weeks and summarize the analysis in a written report.
By completion of this module, students should be able to:
General academic and graduate skills to be obtained:
Data exploration and visualisation, including interpretation of Principal Component Analysis (PCA).
Implementation of machine learning techniques covered in Statistical Pattern Recognition, such as Linear Discriminant Analysis (LDA), Decision trees, Neural networks, Learning Vector Quantisation (LVQ).
Introduction and implementation of further supervised multivariate methods, such as Support Vector Machines (SVMs) and Partial Least Squares Regression (PLSR).
Statistical report writing.
Practical statistical analysis of real data sets, reporting and presentation of the obtained results.
Task | Length | % of module mark |
---|---|---|
Essay/coursework Assignment 1 |
N/A | 10 |
Essay/coursework Assignment 2 |
N/A | 10 |
Essay/coursework Assignment 3 |
N/A | 10 |
Essay/coursework Data Analysis Project |
N/A | 70 |
None
Coursework: Three exercises which are counted towards assessment. Each requires 6 hours of work.
Data Analysis Project: Open assessment, requiring 16 hours of work
(Made available at the end of the Spring term, to be handed in at the end of week 1 of the Summer term.)
Task | Length | % of module mark |
---|---|---|
Essay/coursework Reassessment Data Analysis Project |
N/A | 100 |
Current Department policy on feedback is available in the undergraduate student handbook. Coursework and examinations will be marked and returned in accordance with this policy.
James G, Witten D, Hastie T and Tibshirani R (2013). An Introduction to Statistical Learning with Applications in R. Springer
Everitt B and Hothorn T (2011). An Introduction to Applied Multivariate Analysis with R. Springer