This module is offered in 2020-21.

Aims

The aims of this module are: - to provide a foundation in the theory behind machine learning - to enable students to apply machine learning in practice to solve real-world problems

Learning Outcomes

On successful completion of this module, the student should:

  • be able to demonstrate the main concepts in machine learning
  • demonstrate knowledge of important algorithms in the field and when to use them
  • be able to apply machine learning to solve practical problems

Syllabus

  • Essential concepts
  • Linear methods for regression and classification
  • Nonlinear methods for regression and classification
  • Machine learning methodology
  • Neural networks
  • Support Vector Machines
  • Unsupervised learning methods
  • Practical examples on real data

Compulsory Elements

This module has no compulsory elements beyond those common to all modules (mark of 4 in each assessment component).

Module Delivery

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Last Published: 19 Oct 2020.