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Unit information: Machine Learning (Teaching Unit) in 2021/22

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. Rui Ponte Costa
Open unit status Not open

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.


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

OR COMS30069 Machine Learning (Coursework assessment, 15 credits).

Please note:

COMS30035 is the Teaching Unit for the Machine Learning option.

Single Honours Computer Science and Mathematics and Computer Science students can choose to be assessed by either examination (10 credits, COMS30033) or coursework (15 credits, COMS30069) 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).

School/department Department of Computer Science
Faculty Faculty of Engineering

Description including Unit Aims

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

  • Introduction: tasks, models and features.
  • Binary classification and related tasks. Beyond binary classification.
  • Tree models.
  • Rule models.
  • Linear models.
  • Distance-based models.
  • Probabilistic models.
  • Features.
  • Model ensembles.
  • Machine learning experiments.

Intended Learning Outcomes

Successful completion of this unit will enable students to:

  1. Choose 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. Understand theoretical and practical limitations of machine learning algorithms.

In addition, students assessed by coursework will be enabled 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.

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

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.

Assessment Information

Examination details:

2 hour exam (100%, 10 credits)


Coursework details:

Coursework (100%, 15 credits) - to be completed during a specific period.


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 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.