Detailed module information

Machine Learning

Module aims

This module aims to introduce engineering students to Machine Learning (ML). ML is an increasingly high-profile technology, having huge impact across a number of areas. Future engineers are likely to come across ML in some form in their careers; they could be required to solve problems where ML may provide a solution, or may have to assess an existing ML approach which has been developed. This course therefore aims to provide today's engineering graduates with an understanding of ML techniques, including being able to critically assess whether or not ML is suitable for a problem, as well as what type of algorithm would be best and what may be required to implement it. 

ECTS = 5

Learning outcomes

At the end of the module, the student should be able to:

1. Select a suitable machine learning approach (model and data collection) for a particular problem, including assessing whether machine learning is the best solution at all

2. Write Python code to implement a basic demonstration ML algorithm

3. Investigate the performance of different machine learning algorithms for engineering problems

4. Explain the concepts of machine learning techinques and the corresponding terminology

Module syllabus

Bayesian decision theory and maximum likelihood.

Regression approaches.

Linear discriminant functions.

Support vector machines.

Multilayer neural networks.

Nonparametric techniques.

Nonmetric methods.

Unsupervised learning and clustering.

Teaching methods

Lectures will provide students with understanding of underlying techniques within different machine learning methods. Tutorial time will enable students to practice utilising machine learning libraries and tools in order to develop experience. 

Assessments

Reading list

Resources

• Pattern classification.

  Duda, Richard O.

  2nd ed. / Richard O. Duda, Peter E. Hart, David G. Stork., Wiley

• Pattern recognition and machine learning

  Bishop, Christopher M., author.

  Springer

• A first course in machine learning

  Rogers, Simon, 1979- author.

  Second edition., CRC Press, Taylor & Francis Group

• The elements of statistical learning : data mining, inference, and prediction

  Hastie, Trevor, author.

  2nd ed., Springer

• Data-driven science and engineering : machine learning, dynamical systems, and control

  Brunton, Steven L. (Steven Lee), 1984- author.

  Second edition., Cambridge University Press

• Data-driven science and engineering : machine learning, dynamical systems, and control

  Brunton, Steven L. (Steven Lee), 1984- author.

  Cambridge University Press

Module leaders

Dr Peter Huthwaite