School of Computer Science

Module 06-32250 (2022)

Mathematical Foundations of Artificial Intelligence and Machine Learning

Level 4/M

Ata Kaban Peter Tino Semester 1 20 credits
Co-ordinator: Peter Tino
Reviewer: Ata Kaban

The Module Description is a strict subset of this Syllabus Page.


Mathematics is an integral part of modern approaches to machine intelligence. From the role of linear algebra and calculus in neural network learning models for image classification and speech recognition, to Bayesian approaches to automated disease diagnosis, , to control and reasoning in robotocs, mathematical methods are essential to understand, apply, and advance state-of-the art machine intelligence techniques. This module will introduce a range of mathematical tools and demonstrate how they can be used to understand and solve core machine intelligence tasks, and to analyse the limits of their performance.

Learning Outcomes

On successful completion of this module, the student should be able to:

  • Demonstrate a sound understanding of a range of mathematical tools and their role and importance in artificial intelligence and machine learning
  • Formulate machine intelligence questions using appropriate mathematical tools
  • Use mathematical tools to analyse the performance of machine intelligence methods


  • Main Assessments: 1.5 hour examination (80%) and continuous assessment (20%)
  • Supplementary Assessments: 1.5 hour examination (100%)

Programmes containing this module