Advanced Concepts in Signal Processing (P01760)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : EEL-P-PGACSP

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

? Keywords : pattern recognition, detection and classification, neuronal networks, hidden Markov models, genetic algorithms

Entry Requirements

? Pre-requisites : Prior attendance of Statistical Signal Processing and Discrete-Time Signal Analysis is recommended.

Subject Areas

Home subject area

Postgraduate (School of Engineering and Electronics), (School of Engineering and Electronics, Schedule M)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 3 hour(s) per week for 11 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
10/01/2008	09:00	12:00			Sanderson Drawing Office

All of the following classes

Type	Day	Start	End	Area
Lecture	Thursday	09:00	10:50	KB
Tutorial	Thursday	11:10	12:00	KB

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.