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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Individual Research Project (Biomedical AI) (INFR11197)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeDissertation AvailabilityNot available to visiting students
SCQF Credits80 ECTS Credits40
SummaryThe course is the main MSc project for the proposed MSc (Res) in Biomedical Artificial Intelligence. It will offer students the opportunity to gain in-depth experience of research in an interdisciplinary environment, with joint supervision from an AI and a biomedical specialist.
Course description The course is an individual research project where the students will work independently on an application of Artificial Intelligence to a biomedical problem. All projects will have joint supervision from an AI and a biomedical expert, 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 proposed research, which will form part of the assessment for the responsible innovation course.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Issues in Clinical Data Modelling (INFR11195) AND Group Research Project (Biomedical AI) (INFR11196)
Co-requisites
Prohibited Combinations Other requirements This course is ONLY available to students in the CDT in Biomedical Artificial Intelligence
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  None
Course Start Block 5 (Sem 2) and beyond
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 800 ( Lecture Hours 2, Seminar/Tutorial Hours 2, Dissertation/Project Supervision Hours 20, Feedback/Feedforward Hours 2, Programme Level Learning and Teaching Hours 16, Directed Learning and Independent Learning Hours 758 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% Coursework.

The course assessment will consist of a written dissertation to be independently marked by two examiners, in line with standard MSc requirements. The dissertation will address the following:

- motivation: why is the problem tackled important?
- background: what are the necessary AI and biomedical knowledge?
- originality: what is different in what is proposed?
- implications: what is the impact of the research, both scientifically and more broadly in terms of its societal implications?
Feedback Feedback on assessed coursework will be provided within two weeks, and will include formative comments on work in relation to concepts studied in the course.
Report drafts will be reviewed by peers, the course instructor, the TA, and individual supervisors under a provided rubric.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Be able to independently identify and apply appropriate AI algorithms to specific biomedical problems.
  2. Communicate effectively, both in writing and orally, technical AI concepts to an interdisciplinary audience of biomedical scientists.
  3. Discuss critically the broader societal and ethical implications of AI research in the biomedical field and in the specialised area of the project.
Reading List
None
Additional Information
Graduate Attributes and Skills The course will provide the students with in-depth technical skills in one area of biomedical AI, as well as developing their problem solving and programming skills. As the course entails independent (but supervised) research, the students will gain considerably in terms of self-management skills (timetabling, independence in finding resources, etc). Through the course, the student will gain considerable experience of research in an interdisciplinary environment. Therefore, the students will improve their interdisciplinary communication skills.
KeywordsBAI-IP,CDT-BAI
Contacts
Course organiserDr Diego Oyarzun
Tel:
Email: D.Oyarzun@ed.ac.uk
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
Email: lindsay.seal@ed.ac.uk
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