| CODE | CCE5225 | ||||||||||||
| TITLE | Machine Learning and Pattern Recognition Algorithms | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | 7 | ||||||||||||
| 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 into two parts. The first part introduces linear, polynomial,logistic regression, for the purpose of defining the pattern recognition problem and to outline how a pattern recognition system is designed and built. Following this, higher order features in view of non-linear models are used to motivate the concept using Neural Networks 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 the basic architecture for convolutional neural networks . The second part of this study-unit coversprobabilistic graphical models. . Bayesian classifiers, Bayesian networks, Markov Random Fields, Conditional random fields and Hidden-Markov modelsare covered in this part. Study-Unit Aims: This study-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 Pattern Recognition algorithms available including Neural Networks, Support Vector Machines, Hidden Markov Models and Bayesian Networks. 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: Main Texts: - Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. - Kevin P. Murphy, Machine Learning; A probabilistic Perspective, MIT press, 2012. - Barber, D., Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012. - 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. |
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| ADDITIONAL NOTES | Pre-requisite Qualifications: B.Sc. (Hons); B.Eng; or equivalent | ||||||||||||
| STUDY-UNIT TYPE | Lecture, Independent Study & Tutorial | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Adrian F. Muscat (Co-ord.) Gianluca Valentino |
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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 description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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