Undergraduate Course: Topics in Biomedical Informatics (UG) (INFR11282)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course follows the delivery and assessment of Topics in Biomedical Informatics (INFR11263) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11263 instead. |
| Course description |
This course follows the delivery and assessment of Topics in Biomedical Informatics (INFR11263) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11263 instead.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 9,
Seminar/Tutorial Hours 9,
Feedback/Feedforward Hours 2,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
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| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% Exam |
| Feedback |
One session will be dedicated to an example topic. A practice assessment question will then be set with time to do this and submit. There will be a debrief in a class room session as well as individual feedback on submitted work.
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| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | Topics in Biomedical Informatics (PG INFR11263/ UG INFR11282) & Foundational Biomedical AI research PG (INFR11262) | :120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically assess the challenges associated with biomedical data analysis and modelling across a variety of contexts, in particular with respect to noise in the data, patient stratification and regulatory and ethical issues.
- Critically discuss and compare data acquisition, analysis and modelling protocols.
- Present and explain biomedical data problems and appropriate analysis in one area of biomedicine to an interdisciplinary audience.
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Additional Information
| Graduate Attributes and Skills |
Research and enquiry: problem-solving, critical/analytical thinking, handling ambiguity, knowledge integration.
Personal responsibility and autonomy: ethics and social responsibility, independent learning, self-awareness and reflection, creativity, decision-making.
Communication: interpersonal/teamwork skills; verbal, written, and cross-disciplinary communication. |
| Keywords | TBI (UG),Shadow course |
Contacts
| Course organiser | Dr Andrea Weisse
Tel: (0131 6)51 1211
Email: Andrea.Weisse@ed.ac.uk |
Course secretary | Miss Yesica Marco Azorin
Tel: (0131 6)50 5194
Email: ymarcoa@ed.ac.uk |
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