At the end of this module, the student will be able to:
1. Discuss and describe the applications of ML and how ML more broadly impacts on society.
2. Apply the theory and practice of common ML methodologies and techniques for conducting a machine learning project (e.g., CRISP-DM).
3. Work with common version control software (e.g., Github).
4. Conduct a literature review and work with common editor software for writing research papers and organising related literature (e.g., Latex, Overleaf, Mendeley).
5. Contribute to peer-led knowledge sharing presentations on machine learning research topics.
6. Manage and deliver a software development project within a team environment using appropriate software development methodologies (e.g., agile software development methodologies).
7. Design solutions to a problem specification that are effective, user-friendly and economically viable (e.g., design thinking, user experience, user evaluation and business model canvas).
8. Prototype solutions in successive rounds with user feedback and with increasing realism and detail (e.g., using the lean start-up methodology).
9. Evaluate and test software solutions with users and other stakeholders.
10. Present and demonstrate a working prototype to users and stakeholders.

Not applicable to this module.

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities
• Self-assessment activities

Feedback will be provided through a mixture of individual and group feedback in preparation for key submission dates and following submissions.