Postgraduate Course: Advanced Concepts in Signal Processing (PGEE11020)
Course Outline
| School | School of Engineering |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | 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
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| Course description |
Concepts covered: Classification and recognition; Statistical Inference and learning; Clustering; Feature selection and data reduction (e.g. PCA, ICA); Blind signal separation
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Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2015/16, Available to all students (SV1)
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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 )
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| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% closed-book formal written examination |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| 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.
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Reading List
Duda, Hart and Stork, Pattern Classification.
Theodoridis and Koutroumbas, Pattern Recognition.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | pattern recognition, detection and classification, neuronal networks, hidden Markov models, genetic |
Contacts
| Course organiser | Dr Michael Davies
Tel:
Email: |
Course secretary | Mrs Sharon Potter
Tel: (0131 6)51 7079
Email: |
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