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# Statistical Pattern Recognition - MAT00104M

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

## 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.

• None

## Module will run

Occurrence Teaching period
A Semester 1 2024-25

## 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.

In addition, by the end of the module, M-level students will be able to:

1. Carry out self-directed learning on an advanced (M-level) topic in statistical pattern recognition (e.g. evolutionary algorithms) in order to undertake more complex and involved analyses in an applied context.

2. Critically assess theory and literature on elements of the module content (including additional advanced M-level content).

## 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

• 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

Closed/in-person Exam (Centrally scheduled) 50
Essay/coursework 50

### Special assessment rules

None

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

### Indicative reassessment

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.