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Unit information: Statistical Pattern Recognition in 2018/19

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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 Department of Engineering Mathematics
Faculty Faculty of Engineering

Description

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

  1. acquire a working knowledge of practical data analysis, applied to real world situations.
  2. 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.
  3. 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.
  4. acquire first hand experience in specific algorithms from statistical pattern recognition, including kernel methods, probabilistic graphical models, string analysis, and more.
  5. have performed a real world data analysis task.

Teaching details

Lectures

Assessment Details

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.

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