Undergraduate Course: Computational Modelling and Machine Learning 3 (IBMS09010)
Course Outline
| School | Deanery of Biomedical Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 9 (Year 3 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Computational Modelling and Machine Learning 3 introduces key terminology, principles and approaches in machine learning and computational modelling in the context of biomedical sciences. |
| Course description |
Machine learning approaches and computational models of biological systems are used a range of research settings. This course introduces the principles of machine learning and computational modelling, giving students the knowledge, understanding and practical skills needed to develop, implement and refine machine learning approaches and computational models of biological systems.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Course delivered in China. Only available to students enrolled on BSc Hons Biomedical Informatics. |
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 |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 28,
Supervised Practical/Workshop/Studio Hours 56,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
112 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
This course is 100% assessed by in-course assessment:
Project-based in-course assessments:
1. Computer Modelling in-course assessment (50%)
2. Machine Learning in-course assessment (50%) |
| Feedback |
Guidance and feedback will be available during practical sessions and on discussion forums throughout the course. Feedback from the first project will be formative for the second project.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical and contextualised understanding of the key principles, concepts and terminology used in contemporary machine learning and computational modelling approaches.
- Apply this understanding by developing and implementing appropriate approaches to machine learning and computational modelling in specific biological systems, showing awareness of the approaches¿ capabilities and limitations.
- Exercise autonomy and initiative during the development and implementation of defined projects.
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Additional Information
| Graduate Attributes and Skills |
This course develops the following skills and attributes:
General professional skills: Time management, project management, independence, autonomy, curiosity, self-motivation, critical thinking, reflection, scientific creativity.
Computational and Bioinformatics Skills: Ability to use scientific and statistical analysis software packages, ability to use standard languages and protocols, retrieving and manipulating data from public repositories; expertise in common biomedical informatics software packages, tools, and algorithms
Biology: Selected aspects of molecular biology, cell biology, biochemistry, systems biology, cancer, infection, neuroscience. |
| Keywords | Artificial Intelligence,Machine Learning,Computational Modelling |
Contacts
| Course organiser | |
Course secretary | Miss Natasha Goldie
Tel:
Email: natasha.goldie@ed.ac.uk |
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