Unit name | Statistical Pattern Recognition |
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Unit code | EMATM0012 |
Credit points | 10 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
Unit director | Dr. Colin Campbell |
Open unit status | Not open |
Pre-requisites |
EMAT10100 Engineering Mathematics 1, EMAT20200 Engineering Mathematics 2 (applied statistics), and EMAT20540 Discrete Mathematics 2 (or equivalents) |
Co-requisites |
None. |
School/department | School of Engineering Mathematics and Technology |
Faculty | Faculty of Engineering |
Description: This unit will provide an overview of methods from statistical pattern recognition, used in the discovery and extraction of information from datasets and the construction of decision functions. These types of methods have a wide range of applications from recognising hand-written digits to face identification, bioinformatics and database marketing.
Areas covered will include
Aims: to give students a broad understanding of concepts in statistical pattern recognition as applied across a range of application domains. To give students first-hand experience in specific algorithms from statistical pattern recognition, including kernel methods, probabilistic graphical models, string analysis and more.
On successful completion of the unit, students will
Lectures
A 2 hour written exam (all learning outcomes).
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