Humans are very good at recognising and classifying patterns, and thereby extracting knowledge from their environment. Autonomous systems must also be able to recognise and classify objects from input data obtained from their environment. This module will provide you with a thorough grounding in the theory and application of pattern recognition, classification, categorisation, and concept acquisition Hence, it is particularly suitable for computer science, mathematics and engineering students and any students with some experience in probability and programming.
Full module specification
|Module title:||Pattern Recognition|
|Duration of module:||
Duration (weeks) - term 1: |
0Duration (weeks) - term 2:
11Duration (weeks) - term 3:
In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of the computational methods used in modern data analysis.
In particular, this module aims to impart knowledge and understanding of pattern recognition methods from basic pattern-analysis methods to state-of-the-art research topics; to give you experience of data-modelling development in practical workshops. Furthermore, the module will introduce Neural Networks, Bayesian methods and kernel methods for extracting knowledge from large data sets of patterns where it is important to have explicit rules governing pattern recognition. It will also address problems of coping with noisy and/or missing data as well as temporal and sequential patterns.
ILO: Module-specific skills
- 1. apply advanced and complex principles for statistical pattern recognition to novel data;
- 2. analyse novel pattern recognition and classification problems, establish statistical models for them and write software to solve them;
- 3. use a range of supervised and unsupervised pattern recognition and machine learning techniques to a wide range of problems.
ILO: Discipline-specific skills
- 4. state the importance and difficulty of establishing a principled probabilistic model for pattern recognition;
- 5. utilise a number of complex and advanced mathematical and numerical techniques to solve a wide range of problems and domains.
ILO: Personal and key skills
- 6. identify the compromises and trade-offs which must be made when translating theory into practice;
- 7. critically read and report on research papers;
- 8. conduct small individual research projects.
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Scheduled learning and teaching activities||22||Lectures|
|Scheduled learning and teaching activities||10||Workshop/tutorials|
|Scheduled learning and teaching activities||30||Projects and coursework|
|Guided independent study||88||50 wider reading + 38 coursework preparation|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Coursework workshop report 1||20||1,000-2,000 words||All||Written|
|Coursework workshop report 2||20||1,000-2,000 words||All||Written|
|Coursework workshop report 3||20||1,000-2,000 words||All||Written|
|Coursework workshop report 4||20||1,000-2,000 words||All||Written|
|Coursework workshop report 5||20||1,000-2,000 words||All||Written|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|All above||Coursework (100%)||All||Completed over summer with a deadline in August|
If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.
If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.
- introductory material: motivation for pattern recognition, basic ideas of classification, regression and preliminary concepts;
- statistical preliminaries: Bayes theorem, uncertainty and information entropy, decision theory;
- neural networks: common neural network architectures, MLPs, RBF networks;
- density estimation and discriminants: non-parametric analysis, KNN classifiers, parametric and semi-parametric methods;
- parameter estimation: maximum likelihood estimators, Bayesian learning, optimisation in practice;
-kernel methods: basic convex optimization, support vector machines, kernels and regularization networks;
- unsupervised methods: clustering, dimension reduction
-feature extraction: PCA, sequential forwards/backwards selection, branch and bound;
- handling sequential patterns with Hidden Markov Models;
- Hybrid pattern recognition models.
Indicative learning resources - Basic reading
ELE – http://vle.exeter.ac.uk
- Neural Networks for Pattern Recognition,Bishop, C.,Clarendon Press,1995,001.534 BIS
- Pattern recognition and machine learning,Bishop, C.,Springer,2006,006.4 BIS,978-0387310732
- Statistical Pattern Recognition,Webb, A.,2,Wiley,2002,001.534 WEB,0-470-84513-9
- Pattern Classification and Scene Analysis,Duda and Hart,2nd,Wiley,2002,001.534 DUD/X,0471056693
- NETLAB : algorithms for pattern recognition ,Nabney, Ian T.,Springer,2001,001.534 NAB,1852334401
- Pattern Recognition and Neural Networks,Ripley, Brian D,,CUP,1996,001.534 RIP,0521460867
- Introduction to Statistical Pattern Recognition,Fukunaga, Keinosuke,2nd,Academic Press,1990,001.534 FUK,0122698517
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