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Pattern Recognition, Neural Networks and Deep Learning


Key information

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Module description

Aims and Learning Outcomes

To introduce a range of methods for solving pattern recognition problems, with a particular emphasis on solving classification problems using supervised learning and neural network approaches.

On successful completion of this module, students will:

  • Be able to understand, analyse and assess material of direct relevance to the subject matter as described in advanced textbooks
  • Be aware of common pattern recognition algorithms and methods, be able to describe their main features, and appropriate applications
  • Be able to apply these methods competently to well-defined problems in standard contexts
  • Have developed problem-solving skills to deal with applications which require pattern recognition techniques


  • Discriminant Functions,
  • Biological and Artificial Neural Networks
  • Multilayer Neural Networks
  • Backpropagation
  • Deep Learning
  • Feature Selection and Extraction
  • Support Vector Machines
  • Ensemble Methods
  • Unsupervised Learning and Clustering

Assessment details

70% examination

30% coursework