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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2024/2025

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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Inter-Disciplinary Biomedical Artificial Intelligence Research (INFR11274)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryIn this course you will undertake two group research projects in Artificial Intelligence for Biomedical Innovation that have been co-designed between the CDT and external partners. The projects will be supervised by a small supervisory team of staff with experience in Machine Learning & Biomedicine from within the CDT management team and external partners. The projects will draw on the new skills and knowledge that you have acquired through other courses and will especially develop your skills and experience in working in an inter-disciplinary research group tackling a common challenge in biomedicine using a range of methodologies.
Course description In this course we aim to develop your skills in working in inter-disciplinary research teams in which you will have to communicate and work effectively with people from a diverse range of backgrounds. You will learn how to manage your time and partition tasks to allow the group to progress efficiently through the projects. Many teamwork elements are involved in working on a joint research project and form an essential part of your training as inter-disciplinary scientists, but the opportunity to share knowledge and skills with peers is of key importance. The projects themselves will be proposed by supervisors within the CDT pool of expertise and external partners who often bring unique datasets and sectorial experience to projects. Throughout the projects there will be a focus on developing good scientific practice, including reproducible research methods, and incorporating appropriate consideration of any ethical and societal aspects that may be involved. Students will need to demonstrate the ability to accurately present their research in comprehensive and well-structured jointly prepared research reports.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements UKRI CDT in AI for Biomedical Innovation students only.
Course Delivery Information
Academic year 2024/25, Not available to visiting students (SS1) Quota:  None
Course Start Full Year
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Seminar/Tutorial Hours 4, Dissertation/Project Supervision Hours 30, Feedback/Feedforward Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 160 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Coursework _100_%

The course assessment will consist of two mini group project reports, one in each semester with assessment by a jointly written group project report for each that will be marked by supervisors and moderated by the relevant research theme lead or another suitably qualified and independent person identified by the CDT director or deputy director.
Feedback Students will receive detailed textual feedback from dissertation markers and following the release of the marks have an oral feedback meeting with their supervisors. Students will also present their placement project research to their peers and other staff in a short non-assessed scientific talk. Following the talk supervisors will provide constructive verbal feedback to help students develop and refine their presentation skills.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Coordinate with a team of diverse experts to answer a research question in artificial intelligence for biomedical innovation.
  2. Plan and monitor a coordinated effort to meet milestones and deadlines within a limited timescale.
  3. Communicate novel research results in biomedicine and AI to an interdisciplinary scientific audience orally and in writing.
  4. Identify and synthesise clear background knowledge of the literature surrounding the broader and specific area of the project.
  5. Discuss critically the broader societal and ethical implications of the project research in the biomedical field and in the specialised area of the project.
Reading List
None
Additional Information
Graduate Attributes and Skills Research and Inquiry:
- This project aims to broaden students' exposure to performing real-world application driven research at the interface between biomedicine and computing science. The students will undertake two group projects of c.8-10 weeks co-designed between the CDT team and external partners.

Personal Effectiveness:
- Students will learn to manage their time, plan work, critically assess and troubleshoot research as it progresses and identify learning requirements to manage the delivery of the projects. Students will develop inter-disciplinary and collaborative research skills.

Personal Responsibility:
- Students will begin to build their experience of research in biomedical AI. They will work constructively with the other students in their group and pro-actively contribute their ideas to tackling the problems at hand. They will consider alternative approaches critically, be receptive to challenge from others, and open to advice and guidance to help them develop as a researcher.
- Students will take responsibility for writing at least one specific part of each project report and work collaboratively with colleagues to complete the final drafts on time.

Communication:
- Students will jointly draft project reports for submit a formal written dissertation for the research project and present their research in a short scientific talk to their peers and other member of staff involved in the programme. They will discuss both in feedback sessions with the project supervisory team.
- During the projects students will develop their ability to articulate and explain complex issues to groups of people from varying academic disciplinary backgrounds.
- They will also have the opportunity to develop resources such as training materials, code repositories, and/or outreach activities based on their placement projects where appropriate.
KeywordsBiomedical Innovation,Artificial Intelligence,Inter-disciplinary Research,Machine Learning,AI4BI
Contacts
Course organiserDr Ian Simpson
Tel: (0131 6)50 2747
Email: Ian.Simpson@ed.ac.uk
Course secretary
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