Postgraduate Course: Foundational Biomedical Artificial Intelligence Research (INFR11262)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 30 |
ECTS Credits | 15 |
| Summary | This course introduces key skills and knowledge to incorporate machine learning approaches in biomedical informatics research. The course includes practical training in Python and the statistical programming language R, an exposition of the major sources of biological and biomedical data used in biomedical informatics research, and a series of invited guest lectures highlighting achievements, challenges, and opportunities for research at the interface between computing science and biomedicine. The course also includes masterclasses on practical skills including the use of GitHub, high performance compute clusters, trusted research environments, and from CDT teaching leads in health, artificial intelligence, biomedical science, responsible research & innovation, and entrepreneurship. |
| Course description |
This course introduces key skills and knowledge to incorporate machine learning approaches in biomedical informatics research. The course includes practical training in Python and the statistical programming language R, an exposition of the major sources of biological and biomedical data used in biomedical informatics research, and a series of invited guest lectures highlighting achievements, challenges, and opportunities for research at the interface between computing science and biomedicine. The course also includes masterclasses on practical skills including the use of GitHub, high performance compute clusters, trusted research environments, and from CDT teaching leads in health, artificial intelligence, biomedical science, responsible research & innovation, and entrepreneurship.
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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 |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
300
(
Lecture Hours 27,
Seminar/Tutorial Hours 27,
Feedback/Feedforward Hours 4,
Summative Assessment Hours 4,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 6,
Directed Learning and Independent Learning Hours
230 )
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam 60%
Coursework 40% |
| Feedback |
Students will receive detailed verbal and textual feedback from markers, masterclass presenters and the course organisers for coursework with a written component. For coding and computational methodology assessment we will use peer assessment sessions with course staff. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Programme in Python and R using common data handling and analysis approaches and software environments designed for use with biological and biomedical data such as Biopython and R/Bioconductor.
- Develop software using an IDE and work both individually and in teams with versioning systems following open research practices.
- Design and run computational experiments using the EIDF cluster compute environment.
- Identify and synthesise clear background knowledge from the literature for topics relevant to biomedical innovation research especially those involving artificial intelligence approaches.
- Critically discuss the broader societal and ethical implications of AI research in the biomedical field.
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Reading List
| To be finalised |
Additional Information
| Graduate Attributes and Skills |
Research and Inquiry:
- Students will learn how to synthesise information from the literature to help identify emerging research challenges in biomedical artificial intelligence, state-of-the-art approaches, and have a broad background knowledge across the main learning themes in the field.
- Students will develop critical thinking, analytical, technical, and troubleshooting skills to enable their research in biomedical AI.
Personal Effectiveness:
- Students will learn to manage their time, plan work, and monitor its progress and identify their learning requirements for the future. They will develop interdisciplinary and collaborative research skills and open research best practices.
Personal Responsibility:
- Students will be responsible members of the course cohort providing help, advice, and constructive feedback to peers contributing their unique expertise to a diverse interdisciplinary group of early career researchers. They will be pro-active in engaging with the course and colleagues to maximise the opportunities for peer-peer learning and development of the essential cohort effect for the programme.
- Students will be responsible for seeking help when needed so that staff can fully support them in their studies, not least in helping them navigate University systems for support or seeking extra resource to supplement standard learning opportunities through tutoring and mentorship.
Communication:
- Students will learn key inter-disciplinary working and communication skills through practical sessions, discussions, and group project work.
- Students will develop their ability to articulate and explain complex issues to groups of people from varying academic disciplinary backgrounds.
- Students will have the opportunity to develop resources such as training materials, code repositories, and/or outreach activities based on their learning and mini-projects where appropriate. |
| Keywords | Biomedical Innovation,Artificial Intelligence,Machine Learning,Programming,Bioinformatics,F-BAIR |
Contacts
| Course organiser | Dr Ian Simpson
Tel: (0131 6)50 2747
Email: Ian.Simpson@ed.ac.uk |
Course secretary | |
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