Please note: Due to alternative arrangements for teaching and
assessment in place from 18 March 2020 to mitigate against the restrictions in
place due to COVID-19, information shown for 2019/20 may not always be accurate.
Please note: you are viewing unit and programme information
for a past academic year. Please see the current academic year for up to date information.
| Unit name |
Statistical Pattern Recognition |
| 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 including Unit Aims
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
- Supervised Learning
- Feature selection and extraction
- Unsupervised learning and finding patterns in data.
- Introduction to Bayesian Methods.
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.
Intended Learning Outcomes
On successful completion of the unit, students will
- acquire a working knowledge of practical data analysis, applied to real world situations.
- be able to start from a set of data and deliver patterns and other relevant relations detected in it and assessments about their statistical significance.
- be able to apply general concepts of pattern analysis, that are valid in many domains: algorithmic and statistical principles to be used in different domains.
- acquire first hand experience in specific algorithms from statistical pattern recognition, including kernel methods, probabilistic graphical models, string analysis, and more.
- have performed a real world data analysis task.
Teaching Information
Lectures
Assessment Information
A 2 hour written exam (all learning outcomes).
Reading and References
Main text:
- Statistical Patterns Recognition. Andrew Webb and Keith Copsey, Wiley, 3rd edition, 2011.
Other useful texts:
- Pattern Classification. Duda, Hart, Stork, Wiley, 2000
- The Elements of Statistical Learning. Hastie, Tibshirani and Friedman, Springer, 2001
- Kernel Methods for Pattern Analysis. Shawe-Taylor and Cristianini, Cambridge University Press, 2004.
