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Statistical Pattern Recognition - MAT00100H

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  • Department: Mathematics
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

Provides the theory behind machine learning algorithms as well as practical implementation in R, allowing students to perform statistical analyses of real data, from the formulation of the question to be investigated through to the presentation of the results.

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Module will run

Occurrence Teaching period
A Semester 1 2023-24

Module aims

Provides the theory behind machine learning algorithms as well as practical implementation in R, allowing students to perform statistical analyses of real data, from the formulation of the question to be investigated through to the presentation of the results.

Module learning outcomes

By the end of the module, students will be able to:

  1. Describe and discuss the theoretical foundations of the statistical models and tools considered.

  2. Use various statistical tools to analyse real datasets in R.

  3. Select appropriate machine learning and statistical approaches for specific applications.

  4. Perform independent statistical data analysis on a real data set with a particular research question.

  5. Write up the results of statistical data analysis, employing tables and graphs as appropriate.

Module content

Subject content

  • pattern recognition, measuring objects, features and patterns;

  • data reduction and pre-processing;

  • representation, distance and similarity measures;

  • feature selection, classification and validation;

  • unsupervised learning, clustering algorithms and principal components analysis;

  • Bayesian decision theory;

  • supervised learning, such as linear discriminant analysis and partial least squares;

  • machine learning algorithms, for example neural networks, self-organizing maps and decision trees;

  • combining classifiers

Academic and graduate skills

  • application of pattern recognition and machine learning techniques to a range of problems;

  • use of appropriate scaling, feature weighting and other pre-processing techniques.

Indicative assessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 50
Essay/coursework 50

Special assessment rules

None

Additional assessment information

If a student has a failing module mark, only failed components need to be reassessed.

Indicative reassessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 50
Essay/coursework 50

Module feedback

Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.

Indicative reading

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


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.