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
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Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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| Delivery period: 2014/15 Semester 2, Available to all students (SV1)
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Learn enabled: Yes |
Quota: None |
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Web Timetable |
Web Timetable |
| Course Start Date |
12/01/2015 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 11,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
65 )
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| Additional Notes |
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| Breakdown of Assessment Methods (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | | 1:30 | |
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.
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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: |
Course secretary | Mrs Sharon Potter
Tel: (0131 6)51 7079
Email: |
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