CSC-40072 - Mathematics for AI and Data Science
Coordinator: Edward De Quincey Tel: +44 1782 7 34090
Lecture Time: See Timetable...
Level: Level 7
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2024/25


Available as a Free Standing Elective






Barred Combinations


Description for 2024/25

This module aims to give students from non-mathematical backgrounds an introduction to the mathematical concepts relevant to AI and Data Science.

Intended Learning Outcomes

critically appraise various mathematical approaches to analysing a given data set;: 1,2
select and apply suitable techniques to solve relevant AI and Data Science problems in calculus, linear algebra, and probability;: 1,2
analyse and apply periodic functions;: 1,2
summarise how mathematical approaches can be applied to AI and Data Science problems: 2

Study hours

10 x 2 hours of lectures
10 x 1 hours of practicals/problem classes
90 hours independent study
30 hours report preparation

School Rules


Description of Module Assessment

1: Online Tasks weighted 25%
Set of 5 online weekly tasks
Students will be given 5 weekly short online tasks (equally weighted) to complete that complement the content taught during that week (either via automated methods such as MCQ's/MapleTA or set questions and digital scanning and uploading of answers). This will enable feedback to be given as the course progresses, preparing students for the final report.

2: Report weighted 75%
Report outlining steps and reasons taken to solve a set of problems and presentation of the results.
Report comprising of 3 sections (maximum of 2 pages for each section, excluding appendices) that contains explanations of steps taken to solve a set of problems applied to real world data, including the reasons for taking those steps, presentation of the results and a summary of how the approach taken relates to AI and Data Science: - solution of systems of linear equations - optimisation of a set of given functions of single and multiple variables - analysis of data representing a set of inter-related random variables