Accessibility statement

Intelligent Systems: Machine Learning & Optimisation - COM00026I

« Back to module search

  • Department: Computer Science
  • Credit value: 20 credits
  • Credit level: I
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

Machine Learning & Optimisation.

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

This module introduces the field of Artificial Intelligence, key approaches within the field and philosophical questions such as what it means for a machine to understand. Students will learn the theory and practice of machine learning techniques covering linear regression, simple neural networks, linear algebra and continuous optimisation. Students will see motivating real world problems, the ML techniques required to solve them, the underlying mathematics needed for the technique and their practical implementation. Practicals will be taught using Python, and the group project will introduce the students to a Python-based modern machine learning library such as TensorFlow or PyTorch.

Module learning outcomes

  1. Explain the difference between strong, weak and general AI, understand the relationship between computation and AI, define the machine learning paradigm, and distinguish it from the wider field of AI

  2. Compute partial derivatives and understand the concept of the gradient as a generalisation of the derivative

  3. Express, manipulate and solve systems of linear equations using linear algebra, and apply linear regression and logistic regression

  4. Optimise multivariate functions using gradient descent

  5. Explain the concept of overfitting and how regularisation can be used to prevent it

  6. Construct a basic neural network using a modern machine learning library and learn its weights via optimisation using the backpropagation algorithm

  7. Deconstruct ethical arguments relating to AI and its applications, and appreciate the ethical and privacy implications of machine learning

Indicative assessment

Task % of module mark
Essay/coursework 40
Online Exam -less than 24hrs (Centrally scheduled) 60

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 40
Online Exam -less than 24hrs (Centrally scheduled) 60

Module feedback

Feedback is provided through work in practical sessions, and after the assessments as per normal University guidelines.

Indicative reading

  • Artificial Intelligence: A Modern Approach by Russell and Norvig


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.