Assignments [LO3 and LO4]
Formal examination [LO1, LO2 and LO3].

• Blackboard
• Unconfirmed practical marks when available
• Exam Papers, past 2 years (where applicable)

• provide an accessible introduction to key concepts in machine learning and pattern processing,
• demonstrate the application of machine learning in a number of recent research areas,
• develop an appreciation of the difficulties involved when trying to extract meaning from naturally occurring data with particular reference to data preprocessing, feature extraction, classifier design and efficient learning,
• To prepare students for specialised data-driven subjects at level 3/4 such as natural language processing, speech processing and computational biology.

By the end of the module the student will be able to:

1. Demonstrate how to extract features from data for use by machine learning (ML) techniques.
2. Employ appropriate machine learning techniques to model and analyse complex datasets.
3. Demonstrate the ability to apply ML in various areas of Computer Science, (e.g. in natural language processing, audio/speech processing, biological applications and vision processing), taking into account sustainability issues.
4. Apply Python programming skills to perform data analysis, numerical modelling and visualisation for practical data analytics applications.
5. Critically analyse the benefits of different ML techniques in a given scenario. 

• Motivation and introduction to data driven computing including sustainability issues 
• Multivariate data and probability distributions 
• Classification, including Bayes’ decision theory
• Non-parametric classifiers, including nearest-neighbour classifier
• Feature selection
• Feature generation
• Introduction to deep learning and neural networks
• Unsupervised learning and clustering