Computer Science

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 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. 

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 students experience of data-modelling development in practical workshops.  Neural Networks,  Bayesian methods and kernel-based algorithms will be introduced for extracting knowledge from large data sets of patterns (data mining techniques, Hidden Markov Models) where it is important to have explicit rules governing pattern recognition. Problems of coping with noisy and/or missing data as well as temporal and sequential patterns will be addressed.
 

Module Specific Skills and Knowledge:
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 apply a range of  supervised and unsupervised pattern recognition and machine learning techniques to a wide range of problems.
Discipline Specific Skills and Knowledge:
4 state the importance and difficulty of establishing a principled probabilistic model for pattern recognition;
5 apply a number of complex and advanced mathematical and numerical techniques to a wide range of problems and domains.
Personal and Key Transferable/ Employment Skills and  Knowledge:
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.

Introductory material: 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; Graphical models: Directed acyclic graphs, inference and learning in graphical models; Density estimation and discriminants: non-parametric analysis, KNN classifiers, parametric and semi-parametric methods; Parameter estimation: maximum likelihood estimators, Bayesian learning, optimisation in practice; Unsupervised methods: clustering, PCA, ICA; Feature extraction: PCA, ICA, sequential forwards/backwards selection, branch and bound; Data mining, and rule extraction from, patterns. Handling sequential patterns with Hidden Markov Models; Hybrid pattern recognition models.

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.

ELE – http://vle.exeter.ac.uk

Web based and electronic resources:

Other resources:
 

Reading list for this module: