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CSC-20067 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2024/25 Last Updated: 23 November 2024

CSC-20067 - Machine Learning
Coordinator: Vishwash Batra Tel: +44 1782 7 33114
Lecture Time: See Timetable...
Level: Level 5
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2024/25

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2024/25

Learners will develop an understanding of the fundamentals of Machine Learning and its application to Data Science. The module will focus on developing the skills of a professional data scientist in the context of designing and evaluating data science workflows for a project utilising machine learning. In addition, learners will develop an understanding of the key developments in Data Privacy and Ethical AI and apply their knowledge in the design of their workflow and evaluation of their data science project.

Aims
To provide an introduction to the concepts of statistical data analysis and machine learning. The module provides in-depth training in the use of machine learning tool kits in Python to analyse real world data and to deliver valuable insight that can be used to provide business services.

Intended Learning Outcomes

explain the fundamental vocabulary and concepts in machine learning: 1
apply machine learning to data using packages such as Scikit-Learn: 1
develop a complete data science project workflow which demonstrates understanding of ethical design: 1
evaluate algorithmic decision-making for bias and explainability using performance metrics and fairness testing: 1

Study hours

20 hours of practical: 10 x 2 hour practical sessions
10 hours of online lectures
20 hours coursework preparation
100 hours independent learning

School Rules

None

Description of Module Assessment

1: Report weighted 100%
Structured report outlining planning, implementation and evaluation of a data science project


The University undertakes a continuous review of its teaching and research provision to ensure programmes are of a high quality and changes may be made to modules where it is necessary and reasonable to do so. Please be aware that minor changes to modules, such as a change to an individual assessment, can occur up until enrolment, although we endeavour to give you as much notice as possible of any changes. Details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/legalgovernancecompliance/governance/actcharterstatutesordinancesandregulations/studenttermsconditions/

Last Updated: 23 November 2024