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CODE CCE5225

 
TITLE Pattern Recognition

 
LEVEL 05 - Postgraduate Modular Diploma or Degree Course

 
ECTS CREDITS 5

 
DEPARTMENT Communications and Computer Engineering

 
DESCRIPTION This study-unit describes, derives and evaluates various Pattern Recognition techniques that can be later applied in various domains. This unit emphasises the theoretical and practical aspects of the pattern recognition techniques. At the end of the study-unit the student will be able to select, develop and apply machine learning algorithms to solve particular problems.

The study-unit is organized in three parts. The first part introduces linear, polynomial,logistic regression, information based and probabilistic models for the purpose of defining the pattern recognition problem and to outline how a pattern recognition system is designed and built. Common considerations in pattern recognition, such as over-fitting, under-fitting, regularization, model order and dimensionality are introduced in this part and covered in depth in CCE5107. A review of basic optimisation techniques, appropriate cost functions and the respective analytical and numerical solutions is covered in this part.

The second part of this study-unit covers higher order features in view of non-linear models and motivates the concept using Neural Networks, followed by kernel methods and support vector machines. The feed-forward neural network is covered in depth, including the back-propagation algorithm, and an analysis of what is being learnt by the network. This part ends with generalizing regression and kernel methods to the linear basis function model.

The third part of this study-unit covers density estimation, probabilistic and Bayesian methods. It therefore starts with a review of statistics and probability. Maximum likelihood is formalized in Gaussian mixture models, and the regression problem is viewed from a probabilistic perspective, frequentist and Bayesian approach, to gain insight in the minimization of error functions and regularization. Bayesian classifiers (MAP and Naïve Bayes) are covered in this part. The Expected Maximisation algorithm is motivated in GMM models and is derived from both a probabilistic and a Bayesian perspective.

This part ends with a theoretical foundation in graphical probabilistic models; Bayes Networks and Hidden Markov Models.

Study-unit Aims:

This unit covers various Pattern Recognition techniques that can be used in various areas. The study unit gives the student the basic statistical methodology in its various aspects for the student to be able to develop machine learning algorithms to solve particular problems.

Learning Outcomes:

1. Knowledge & Understanding:
By the end of the study-unit the student will be able to:

- Compare and describe different concepts of Pattern Recognition and the features to be used for the classification;
- Analyse and demonstrate the application of a number of Machine Learning algorithms available including Neural Networks, Support Vector Machines and Hidden Markov Models.

2. Skills:
By the end of the study-unit the student will be able to:

- Implement statistical representation of a problem;
- Design features and classifiers to solve a particular problem.

Main Text/s and any supplementary readings:

- Kevin P. Murphy, Machine Learning; A probabilistic Perspective, MIT press, 2012.
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
- Tom Mitchell, Machine learning, McGraw Hill, 1997.
- R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification (2nd ed.), John Wiley and Sons, 2001. ISBN: 978-0-471-05669-0.
- Simon Haykin, Neural Networks and Learning Machines, 3rd Edition, Pearson, 2009.

 
ADDITIONAL NOTES Pre-requisite Qualifications: B.Sc. (Hons); B.Eng; or equivalent

 
STUDY-UNIT TYPE Lecture, Independent Study & Tutorial

 
METHOD OF ASSESSMENT
Assessment Component/s Resit Availability Weighting
Practical Yes 40%
Examination (2 Hours) Yes 60%

 
LECTURER/S Adrian F. Muscat

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the study-unit description above applies to the academic year 2017/8, if study-unit is available during this academic year, and may be subject to change in subsequent years.
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