COM2004 Data Driven Computing
| Summary |
This module is intended to serve as an introduction to
machine learning and pattern processing, but with a clear
emphasis on applications. The module is themed around the
notion of data as a resource; how it is acquired, prepared
for analysis and finally how we can learn from it. The
module will employ a practical Python-based approach to try
and help students develop an intuitive grasp of the
sophisticated mathematical ideas that underpin this
challenging but fascinating subject. |
| Session |
Autumn 2025/26 |
| Credits |
20 |
| Assessment |
Assignments [LO3 and LO4]
Formal examination [LO1, LO2 and LO3]. |
| Lecturer(s) |
Dr Matthew Ellis, Dr Tong Liu & Dr Xingyi Song |
| Resources |
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| Aims |
This unit aims to:
- 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.
|
| Learning Outcomes |
By the end of the module the student will be able to:
- Demonstrate how to extract features from data for use
by machine learning (ML) techniques.
- Employ appropriate machine learning techniques to model and analyse complex datasets.
- 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.
- Apply Python programming skills to perform data analysis, numerical modelling and visualisation for practical data analytics applications.
|
| Content |
This module will cover:
- 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
|
| Restrictions |
This module cannot be taken with COM3004. |
| Teaching Method |
Lectures and laboratory classes. |
| Feedback |
Feedback following the assignment and during labs/lectures for weekly formative exercise questions. |
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