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Unit information: Machine Learning Paradigms in 2021/22

Unit name Machine Learning Paradigms
Unit code COMSM0025
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Professor. Peter Flach
Open unit status Not open




School/department Department of Computer Science
Faculty Faculty of Engineering

Description including Unit Aims

This unit gives an in-depth overview of Machine Learning, exploring both unity and diversity among different ML paradigms and why this diversity is needed and how it can be exploited. The paradigms covered include: Introduction: tasks, models and features; Tree and Rule models; Linear and Distance-based models; Probabilistic models; Model ensembles; Deep learning. The unit will provide students with a solid analytical and practical framework for further work in data-driven AI.

Intended Learning Outcomes

After successfully completing this unit, you will be able to

  1. Choose an appropriate learning algorithm for a given problem;
  2. Use machine learning algorithms in solving classification problems;
  3. Understand theoretical and practical limitations of machine learning.

Teaching Information

Teaching will be delivered through a series of mostly synchronous sessions, including lectures, seminars, practical activities, discussion groups and self-directed exercises.

Assessment Information

1 Summative Assessment, 100% - Coursework. This will assess all ILOs.


If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMSM0025).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the Faculty workload statement relating to this unit for more information.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.