Postgraduate Course: Machine Learning in Signal Processing (MSc) (PGEE11175)
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 machine learning, pattern recognition, classification and adaption in the analysis of complex signals and data sets.
Introduction to Machine Learning, Pattern Recognition, Detection, Classification, Modelling, Statistical Inference, Cluster Analysis, Neural Networks, Deep learning, 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
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2019/20, Available to all students (SV1)
|
Quota: 96 |
| 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) |
Written exam 100% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- An understanding of pattern recognition and machine learning and will learn how to apply these methods to the processing of a broad class of signals;
- 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 |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Pattern recognition,neuronal networks,hidden Markov models,machine learning,detection,classification |
Contacts
| Course organiser | Prof Sotirios Tsaftaris
Tel: (0131 6)50 5796
Email: S.Tsaftaris@ed.ac.uk |
Course secretary | Mrs Megan Inch-Kellingray
Tel: (0131 6)51 7079
Email: M.Inch@ed.ac.uk |
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