Programme/Approved Electives for 2020/21
Available as a Free Standing Elective
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.
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;: 1,2select and apply suitable techniques to solve relevant AI and Data Science problems in calculus, linear algebra, and probability;: 1,2analyse and apply periodic functions;: 1,2summarise how mathematical approaches can be applied to AI and Data Science problems: 2
10 x 2 hours of lectures10 x 1 hours of practicals/problem classes90 hours independent study30 hours report preparation
1: Online Tasks weighted 25%
Description of Module Assessment
Set of 5 online weekly tasksStudents 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