Postgraduate Course: Advanced Concepts in Signal Processing (PGEE11020)
|School||School of Engineering
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|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, Latent Variable Models, Independent Component Analysis, Hidden Markov Models, Applications to Speech, Audio and Image Data
Concepts covered: Classification and recognition; Statistical inference and learning; Clustering; Feature selection and data reduction (e.g. PCA, ICA); Blind signal separation
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2017/18, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||100% closed-book formal written examination
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
| Students will acquire 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. 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.
|Duda, Hart and Stork, Pattern Classification.|
Theodoridis and Koutroumbas, Pattern Recognition.
|Graduate Attributes and Skills
|Keywords||pattern recognition,neuronal networks,hidden Markov models,machine learning,detection,classification
|Course organiser||Dr Sotirios Tsaftaris
Tel: (0131 6)50 5796
|Course secretary||Miss Megan Inch
Tel: (0131 6)51 7079