Studying at York
Statistical Pattern Recognition - MAT00104M
- 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.
Related modules
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
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:
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Describe and discuss the theoretical foundations of the statistical models and tools considered.
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Use various statistical tools to analyse real datasets in R.
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Select appropriate machine learning and statistical approaches for specific applications.
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Perform independent statistical data analysis on a real data set with a particular research question.
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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:
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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.
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Critically assess theory and literature on elements of the module content (including additional advanced M-level content).
Module content
Subject content
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pattern recognition, measuring objects, features and patterns;
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data reduction and pre-processing;
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representation, distance and similarity measures;
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feature selection, classification and validation;
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unsupervised learning, clustering algorithms and principal components analysis;
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Bayesian decision theory;
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supervised learning, such as linear discriminant analysis and partial least squares;
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machine learning algorithms, for example neural networks, self-organizing maps and decision trees;
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combining classifiers
Academic and graduate skills
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application of pattern recognition and machine learning techniques to a range of problems;
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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