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for a past academic year. Please see the current academic year for up to date information.
| Unit name |
Pattern Analysis and Statistical Learning |
| Unit code |
EMATM1400 |
| Credit points |
10 |
| Level of study |
M/7
|
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24)
|
| Unit director |
Dr. De Bie |
| Open unit status |
Not open |
| Pre-requisites |
EMAT10100 or EMAT10702 or equivalent |
| Co-requisites |
None |
| School/department |
Department of Engineering Mathematics |
| Faculty |
Faculty of Engineering |
Description including Unit Aims
This unit provides first hand experience about the problem of analysing complex real world datasets, like those provided by biology, web, engineering, and many other domains. Students will be exposed to the most recent approaches based on statistical methods, and optimization theory, and to state of the art algorithms. They will also experience real examples of data analysis, based on actual case studies.
Aims:
- To give students a broad understanding of concepts in pattern analysis and statistics as applied across a range of application domains.
- To give students first hand experience in specific algorithms from statistical learning and pattern recognition, including kernel methods, probabilistic graphical models, string analysis, and more.
- To teach student the practical application of matlab to pattern analysis problems.
Intended Learning Outcomes
- Students will access this unit with a basic knowledge of probability and will acquire working knowledge of practical data analysis, in real world situations.
- They will 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.
- They will learn general concepts about pattern analysis, that are valid in many domains: algorithmic and statistical principles to be used in different domains.
- They will also acquire first hand experience in specific algorithms from statistical learning and pattern recognition, including kernel methods, probabilistic graphical models, string analysis, and more.
- They will see how a real world data analysis task is performed, by practicing with real data and matlab.
Teaching Information
Lectures
Assessment Information
- 2-hour written examination 50% (learning outcomes 1-4)
- Coursework 50% (learning outcomes 1-5)
Reading and References
Duda, Hart, Stork, Wiley, 2000
- The Elements of Statistical Learning
Hastie, Tibshirani, Friedman, Springer, 2001
- Kernel Methods for Pattern Analysis
Shawe-Taylor, Cristianini, Cambridge University Press, 2004.
