Postgraduate Course: Placement Dissertation Project (INFR11264)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Dissertation |
Availability | Not available to visiting students |
| SCQF Credits | 60 |
ECTS Credits | 30 |
| Summary | This course comprises an individual placement research project as part of the UKRI CDT in AI for Biomedical Innovation. It offers students the opportunity to gain in-depth experience of research in an interdisciplinary environment, with joint supervision in AI and biomedical science ordinarily conducted in an external partner organisation. |
| Course description |
The course is an individual research project where students will work independently on an application of Artificial Intelligence to a biomedical problem in an external partner organisation associated with the UKRI CDT in AI for Biomedical Innovation. All projects will have joint supervision between the University and the external partner that jointly provides adequate supervision of both AI and biomedical components, enabling the student to work in an interdisciplinary environment and be embedded within the application. A particular feature of this course will be its attention to the societal and ethical aspects of the research, which will form part of the assessment.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | UKRI CDT in AI for Biomedical Innovation students only. |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 5 (Sem 2) and beyond |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
600
(
Seminar/Tutorial Hours 2,
Dissertation/Project Supervision Hours 24,
Feedback/Feedforward Hours 2,
Programme Level Learning and Teaching Hours 12,
Directed Learning and Independent Learning Hours
560 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam _____%
Coursework _100_%
The course assessment will consist of a written dissertation to be independently marked by two examiners, in line with standard MSc requirements. |
| 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:
- Identify and synthesise clear background knowledge of the literature surrounding the broader and specific area of the project.
- Co-design a research project in consultation with supervisors and successfully undertake a time constrained systematic piece of scholarly work according to a plan, and provide a well-structured, thorough dissertation report with critical analysis and interpretation.
- Independently identify and apply appropriate AI algorithms to specific biomedical problems.
- Communicate effectively, both in writing and orally, technical AI concepts to an interdisciplinary audience.
- Discuss critically the broader societal and ethical implications of AI research in the biomedical field and in the specialised area of the project.
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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 student will co-design and undertake a 12-week mini research project which will ordinarily take place away from the University an external programme partner giving them experience of research in a different environment.
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 project. The student will develop inter-disciplinary and collaborative research skills building on programme level training in advance of the placement project being undertaken.
Personal Responsibility:
- Students will begin to build their independence both in learning and their research. They will take responsibility for their project and be able to argue the case for the approach they have adopted. They will consider alternative approaches critically, be receptive to challenge from others, and open to advice and guidance to help them develop as an independent researcher.
Communication:
- Students will 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 their supervisors.
- During the project the 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. |
| Keywords | Biomedical Innovation,Artificial Intelligence,Placement Project,Machine Learning,AI4BI |
Contacts
| Course organiser | Dr Ian Simpson
Tel: (0131 6)50 2747
Email: Ian.Simpson@ed.ac.uk |
Course secretary | |
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