CSC-40072 - Mathematics for AI and Data Science
Coordinator: Sangeeta Sangeeta Tel: +44 1782 7 33079
Lecture Time: See Timetable...
Level: Level 7
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2020/21

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2020/21

This module aims to give students from non-mathematical backgrounds an introduction to the mathematical concepts relevant to AI and data science. It will provide students with the necessary knowledge and skills to tackle real world AI and data science problems including topics such as quadratic equations; vectors, matrices; linear questions and multiple variables; functions and probability.

Aims
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

None

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