Machine Learning Algorithms - COMP8362

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Autumn Term 7 15 (7.5) checkmark-circle


In this module you learn what is meant by machine learning and how to explain the key algorithms (especially the backpropagation algorithm for learning deep neural networks) and mathematical equations that underlie them. You also familiarise yourself with a range of machine learning algorithms and their inner mechanisms. Using state-of-the-art software technology apply these algorithms to the solution of problems. You will study the existing machine learning implementations of selected algorithms, and you will also engage in implementation of algorithms and procedures relevant to machine learning.


Contact hours

Private Study: 111
Contact Hours: 39
Total: 150

Method of assessment

13.1 Main assessment methods
Two simulation assessments (individual; 12 hours; 20% total)
Examination (2 hours; 80%)

Reassessment methods
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Indicative reading

The most up to date reading list for each module can be found on the university's reading list pages.

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
8.1 Describe what is meant by machine learning, list a number of types of machine learning algorithms (e.g. neural networks) and give a brief description of each together with some examples of their (actual or potential) applications.
8.2 Select the appropriate machine learning paradigm for a particular problem and be able to justify this choice based on knowledge of the properties and potential of this paradigm. To be able to compare the general capabilities of a number of such paradigms and give an overview of their comparative strengths and weaknesses.
8.3 Explain the mathematical equations that underlie selected machine learning algorithms, both the equations/ algorithms that define predictions or decisions, and those that define learning (e.g. the backpropagation algorithm for neural networks).
8.4 Analyse learning phenomena from the point of view of their being computational systems. To be able to take these phenomena and identify the features which are important for computational problem solving by learning from data or from simulation.
8.5 Build machine learning models (e.g. neural networks) using state-of-the-art simulation technology and apply them to the solution of problems. In particular, to select from the canon of learning algorithms which is appropriate for a particular problem domain.
8.6 Discuss examples of challenges related to learning from data. To be able to analyse related systems not directly studied in the course in a similar fashion.
8.7 Discuss examples of machine learning models as applied to various tasks.
8.8 Demonstrate knowledge and explain the key details of one or more advanced specialised topic in machine learning.

The intended generic learning outcomes.
On successfully completing the module students will be able to:
9.1 Utilize the library, exploit online resources and internet sites to support investigations into these areas.
9.2 Demonstrate analytical skills in respect to subsymbolic systems.
9.3 Demonstrate enhanced time management and organisation skills.
9.4 Set learning goals and identify resources for the purpose of continuing professional development.
9.5 Demonstrate originality in tackling and dealing with challenges related to the process of applying machine learning techniques in a broader context of scientific enquiry.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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