Skip to main content

Unit information: Introduction to Artificial Intelligence in 2023/24

Unit name Introduction to Artificial Intelligence
Unit code EMATM0044
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Houghton
Open unit status Not open
Units you must take before you take this one (pre-requisite units)

Worksheets and practice exercises for the course will use the Python programming language. Students are expected to have knowledge of basic programming concepts such as variables, functions, branching and looping, and datatypes such as int, float, strings, arrays, lists, at the level of EMAT10007 or EMATM0048. Guidance for moving from other languages will be provided as needed.

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


Units you may not take alongside this one


School/department School of Engineering Mathematics and Technology
Faculty Faculty of Engineering

Unit Information

This unit will provide a broad introduction to AI for MSc students in SCEEM. It will provide an overview of the most established AI and Machine Learning approaches and paradigms and give students the opportunity to implement AI algorithms and use relevant software tools. Areas covered will included supervised learning (classification and regression, e.g. neural networks), unsupervised learning (clustering), probabilistic methods (e.g. Bayesian networks and Markov decision processes), genetic algorithms, and multi-agent systems.

Your learning on this unit

Upon successful completion of the course, students will be able to:

  1. Be able to explain basic concepts and assumptions underpinning key AI algorithms
  2. Rigorously compare the performance of competing methods.
  3. Implement AI algorithms in a suitable programming language and toolboxes.
  4. Apply machine learning to analyse data.
  5. Modelling the behaviour of autonomous systems.

How you will learn

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions or online computer laboratories and problem sheets.

How you will be assessed

50% Coursework, covering the use of a dataset to make predictions and selection of the most appropriate models for the task, and ethical considerations in the creation and use of datasets. (ILOs 2, 3, 4)

50% in-class tests, covering understanding of algorithms and methods. (ILOs 1, 2, 5)


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. EMATM0044).

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.