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Artificial Intelligence & Machine Learning - COM00143M

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  • Department: Computer Science
  • Credit value: 15 credits
  • Credit level: M
  • Academic year of delivery: 2024-25

Module summary

This module explores the field of artificial intelligence along with the principal ideas and techniques in three core topic areas: problem solving, knowledge representation and machine learning.

Related modules

Pre-requisite modules

Module will run

Occurrence Teaching period
A Online Teaching Period 3 2024-25

Module aims

This module will explore the field of artificial intelligence and study the principal ideas and techniques in three core topic areas: solving problems by searching, logic, and machine learning. It will help students to develop practical skills in AI problem-solving and to understand the legal and ethical implications of AI for business and society.

Module learning outcomes

After completing the module, students should be able to:

  1. Critically analyse the principal ideas and techniques of Artificial Intelligence,

  2. Apply AI search to solve problems that may be represented as states, transitions and goals,

  3. Design logical systems that are able to represent knowledge and make decisions,

  4. Apply machine learning techniques to create AI agents that can learn from observed data,

  5. Critically evaluate the societal impact of AI including legal and ethical issues.

Module content

Topics:

  1. Artificial intelligence and its application areas.

  2. Basic AI search algorithms.

  3. More advanced AI search algorithms.

  4. Basics of logical systems.

  5. More advanced topics in propositional logical systems.

  6. Overview of the three main types of machine learning: supervised, unsupervised, and reinforcement.

  7. Theory and examples of supervised learning on a range of models.

Indicative assessment

Task % of module mark
Online Exam -less than 24hrs (Centrally scheduled) 100.0

Special assessment rules

None

Additional assessment information

Please note all summative assessments (original or resit) are via online exam. Please refer to your module page on Canvas.

Indicative reassessment

None

Module feedback

Feedback will be in line with University policy.

Indicative reading

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd ed. 2009)


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.