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Unit information: Machine Learning (Teaching Unit) in 2023/24

Unit name Machine Learning (Teaching Unit)
Unit code COMS30035
Credit points 0
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Cussens
Open unit status Not open
Units you must take before you take this one (pre-requisite units)

COMS10016 Imperative and Functional Programming and COMS10017 Object-Oriented Programming and Algorithms I or equivalent.

COMS10014 Mathematics for Computer Science A and COMS10013 Mathematics for Computer Science B or equivalent.

COMS20011 Data-Driven Computer Science or equivalent.

Programming: Python or another major programming language (Java, C).

Maths: basic linear algebra, basic statistics, some calculus, some discrete maths.

Units you must take alongside this one (co-requisite units)

EITHER Assessment Units COMS30033 Machine Learning (Examination assessment, 10 credits)

OR COMS30077 Machine Learning (Examination and Coursework assessment, 20 credits).

Please note:

COMS30035 is the Teaching Unit for the Machine Learning option.

Single Honours Computer Science and some Joint Honours students can choose to be assessed by either examination (10 credits, COMS30033) or examination and coursework (20 credits, COMS30077) by selecting the appropriate co-requisite assessment unit.

Any other students that are permitted to take the Machine Learning option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMS30033).

Units you may not take alongside this one
  • None.
School/department School of Computer Science
Faculty Faculty of Engineering

Unit Information

Why is this unit important

Machine Learning is the science of how we can build abstractions of the world from data and use them to solve problems in a data-driven way.

How does this unit fit into your programme of study

This is an optional unit that can be taken in Year 3.

Your learning on this unit

An overview of content

This unit introduces the field of Machine Learning,and teaches how to create and use software that improves with experience. Examples include:

  • Introduction: machine learning concepts
  • Revisiting regression, Bayesian regression
  • Classification (including intro to neural networks)
  • Graphical models
  • Kernel machines
  • Clustering
  • Principal and independent component analysis
  • Sequential data
  • Hidden Markov Models
  • Trees and model ensembles

How will students, personally, be different as a result of the unit

This unit will equip students with the theoretical concepts and practical implementations of the most used machine learning techniques.

Learning Outcomes

On successful completion of this unit, students will be able to:

1. Select an appropriate learning algorithm for a given problem.

2. Use machine learning algorithms in solving classification and prediction problems in a data-driven way.

3. Comprehend theoretical and practical limitations of machine learning algorithms.

When the unit is taken with the associated 20 credit option that includes coursework, students will also be able to:

1. Use existing machine learning libraries to implement a fully operational machine learning system.

2. Empirically assess the performance of the system.

3. Report on and justify the approach taken, and interpret the empirical results.

How you will learn

The students will learn through a combination of lectures and labs designed to complement the lectures. If taken with coursework, the unit also provides weekly coursework support sessions.

How you will be assessed

Tasks which help you learn and prepare you for summative tasks (formative):

Teaching will take place over Weeks 1-7, with coursework support in weeks 9-11 and for students assessed by examination, consolidation and revision sessions in Weeks 12.

Tasks which count towards your unit mark (summative):

2 hour exam (10 credits: COMS30033 100%; COMS30077 – 50%)

In addition, students taking add COMS30077 will also take a coursework in weeks 9-11(50%, equiv to 10 credits,).

When assessment does not go to plan

Students will retake relevant assessments in a like-for-like fashion in accordance with the University rules and regulations.


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. COMS30035).

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 University 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. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates within the Regulations and Code of Practice for Taught Programmes.