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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2010/2011
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DRPS : Course Catalogue : School of Engineering : Postgrad (School of Engineering)

Postgraduate Course: Advanced Concepts in Signal Processing (PGEE11020)

Course Outline
School School of Engineering College College of Science and Engineering
Course type Standard Availability Available to all students
Credit level (Normal year taken) SCQF Level 11 (Postgraduate) Credits 10
Home subject area Postgrad (School of Engineering) Other subject area None
Course website None Taught in Gaelic? No
Course description This course aims to introduce techniques for performing pattern recognition, classification and adaption in the analysis of complex signals and data sets.
Introduction to Pattern Recognition, Detection, Classification, Modelling. Statistical Inference, Cluster Analysis, Neural Networks, Latent Variable Models, Independent Component Analysis, Hidden Markov Models, Applications to Speech, Audio and Image Data
Entry Requirements
Pre-requisites It is RECOMMENDED that students have passed Statistical Signal Processing (PGEE11027) AND Discrete-Time Signal Analysis (PGEE11026)
Co-requisites
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
Pre-requisites None
Displayed in Visiting Students Prospectus? Yes
Course Delivery Information
Delivery period: 2010/11 Semester 2, Available to all students (SV1) WebCT enabled:  Yes Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
King's BuildingsLecture1-11 09:00 - 10:50
King's BuildingsTutorial1-11 11:10 - 12:00
First Class Week 1, Thursday, 09:00 - 10:50, Zone: King's Buildings. Classroom 10, Alrick Building
Exam Information
Exam Diet Paper Name Hours:Minutes Stationery Requirements Comments
Main Exam Diet S2 (April/May)1:3012 sides
Summary of Intended Learning Outcomes
Students will acquire an understanding of pattern recognition and adaptive methods and will learn how to apply these methods to the processing of a broad class of signals.
By the end of the module the student will be able to: Recall a range of techniques and algorithms for pattern recognition and intelligent processing of signals and data, including neural networks and statistical methods. Derive and analyse properties of these methods. Discuss the relative merits of different techniques and approaches. Implement some of these techniques in software (e.g. Matlab). Apply these methods to the analysis of signals and data.
Assessment Information
100% closed-book formal written examination
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus Not entered
Transferable skills Not entered
Reading list Not entered
Study Abroad Not entered
Study Pattern Not entered
Keywords pattern recognition, detection and classification, neuronal networks, hidden Markov models, genetic
Contacts
Course organiser Dr Michael Davies
Tel:
Email: mike.davies@ed.ac.uk
Course secretary Mrs Kim Orsi
Tel: (0131 6)50 5687
Email: Kim.Orsi@ed.ac.uk
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copyright 2011 The University of Edinburgh - 31 January 2011 8:05 am