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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2017/2018

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DRPS : Course Catalogue : School of Engineering : Postgrad (School of Engineering)

Postgraduate Course: Advanced Concepts in Signal Processing (PGEE11020)

Course Outline
SchoolSchool of Engineering CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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
Course description Concepts covered: Classification and recognition; Statistical Inference and learning; Clustering; Feature selection and data reduction (e.g. PCA, ICA); Blind signal separation
Entry Requirements (not applicable to Visiting Students)
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
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Seminar/Tutorial Hours 11, Formative Assessment Hours 1, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 62 )
Assessment (Further Info) Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment) 100% closed-book formal written examination
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
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.
Reading List
Duda, Hart and Stork, Pattern Classification.
Theodoridis and Koutroumbas, Pattern Recognition.
Additional Information
Graduate Attributes and Skills Not entered
Keywordspattern recognition,detection and classification,neuronal networks,hidden Markov models,genetic
Contacts
Course organiserDr Sotirios Tsaftaris
Tel: (0131 6)50 5796
Email: S.Tsaftaris@ed.ac.uk
Course secretaryMiss Megan Inch
Tel: (0131 6)51 7079
Email: M.Inch@ed.ac.uk
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