Postgraduate Course: Foundations of Machine Learning for Health and Social Care (HEIN11098)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course introduces students to the basic theory of machine learning, whilst also helping to develop the skills necessary to implement and interpret the outputs of machine learning algorithms in Python. Course content is grounded in the context of health and social care. After completing the course, students will understand the difference between supervised (classification and regression) and unsupervised (clustering and dimensionality) techniques. |
| Course description |
The use of machine learning within society, and importantly within the field of health and social care, is rapidly increasing. From being able to personalise health treatment plans based on large datasets to streamlining service delivery, the integration of machine learning has the potential, not only to improve existing healthcare pathways, but also to improve prevention (for example via data-driven targeting of behaviour change messages, so that each person receives a type of message effect to them) and lead to earlier diagnosis (such as targeting early traction testing at people within a higher risk category).
In this five-week online learning course, the following content will be covered to provide students with foundational knowledge and practical skills to understand and apply machine learning prediction tools within a health and social care context. Case studies are presented in weeks 2-5 to match the content of the course and aim to be based on papers where data is available for students to investigate and utilise.
Week 1: Understanding the need for machine learning
- differentiating between artificial intelligence (AI), machine learning (ML) and deep learning (DL)
- ethical implications and considerations of ML
- generative AI and its use in health and social care
Week 2: Quantitative-based methods (regression)
- reminder of normal linear regression
- ridge and lasso regression
- Case Study 2
Week 3: Qualitative-based problems (classification)
- logistic regression
- decision trees
- Case Study 1
Week 4: Unsupervised learning
- differentiating unsupervised from supervised learning
- clustering techniques (k-means, DBSCAN)
- Case Study 3
Week 5: Deep learning
- neural networks
- training processes in deep learning
- Case Study 4
Students can expect each week: a series of recorded lecture materials split by the topics above, a discussion board prompt, a code-along and theory practice questions. In addition, there will be a live session each week for students to work together on a set of programming exercises, and Q&A sessions throughout the course.
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Course Delivery Information
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| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 1,
Online Activities 35,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written exam 0%, Coursework 100%, Practical exam 0%
Final written assignment |
| Feedback |
Formative feedback will be provided throughout the course, for example, during live question and answer sessions, quizzes and discussion boards. Formative tasks will also be offered before the student submits their summative assessed course work. All assignments will be marked, and feedback will be provided within fifteen working days (where possible). |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of the significance and diverse applications of machine learning across multiple health and social care settings, recognising current forefront developments and challenges
- Develop original and creative responses to problems, by applying a range of classification and regression techniques in machine learning to complex, varied data sources within health and social care contexts
- Differentiate between the need and application of supervised and unsupervised machine learning techniques
- Undertake a critical reflection of the ethical considerations and impact in the use of machine learning, to make informed judgments about health and social care, including issues not addressed by current codes or practices
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Reading List
Course notes will be provided. The following texts are suggested for a deeper understanding of practices and issues of machine learning, particularly in a health and social care context. All texts are available through the library systems.
Programming:
1. Introduction to Machine Learning with Python: A Guide for Data Scientists by Sebastian Raschka and Vahid Mirjalili
2. Practical AI for Healthcare Professionals by Abinav Suri
Bias and Fairness in Machine Learning:
1. Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard
2. The Ethics of Artificial Intelligence in Healthcare: From Hands-on Practice to Policymaking by Eike-Henner Kluge
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Additional Information
| Graduate Attributes and Skills |
1) Mindsets:
Enquiry and lifelong learning
Students on this course will be encouraged to seek out ways to develop their expertise in probability and statistics. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to resolve statistical issues as they arise in health and social care systems.
Aspiration and personal development
Students will be encouraged to draw on the quality, depth and breadth of their experiences to expand their potential and identify areas in which they wish to develop and grow. Students will also be encouraged to understand their responsibility within, and contribute positively, ethically and respectfully to the health and social care community, while acknowledging that different students and community members will have other priorities and goals.
Outlook and engagement
Students will be expected to take responsibility for their learning. Students will be asked to use their initiative and experience, often explicitly relating to their professional, educational, geographical or cultural context to engage with and enhance the learning of students from the diverse communities on the programme. Students will also be asked to reflect on the experience of their peers and identify opportunities to enhance their learning.
2) Skills:
Research and enquiry
Students will use self-reflection to seek out learning opportunities. Students will also use the newly acquired knowledge and critical assessment to identify and creatively tackle problems and assimilate the findings of primary research and peer knowledge in their arguments, discussions and assessments.
Personal and intellectual autonomy
Students will be encouraged to use their personal and intellectual autonomy to critically evaluate learning materials and exercises. Students will be supported through their active participation in self-directed learning, discussion boards and collaborative activities to critically evaluate concepts, evidence and experiences of peers and superiors from an open-minded and reasoned perspective.
Personal effectiveness
Students will need to be effective and proactive learners that can articulate what they have learned, and have an awareness of their strengths and limitations, and a commitment to learning and reflection to complete this course successfully.
Communication
Effective data scientists' practitioners in the health and social care sector require excellent oral and written communication, presentation and interpersonal skills. The structure of the interactive (problem-based learning examples, discussion boards and collaborative activities) and assessment elements incorporate constant reinforcement and development of these skills.
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| Keywords | Health and social care,python programming,machine learning |
Contacts
| Course organiser | Miss Michelle Evans
Tel: (0131 6)51 5440.
Email: michelle.evans@ed.ac.uk |
Course secretary | Ms Rebecca Sewell
Tel: (0131 6)51 7112
Email: Rebecca.Sewell@ed.ac.uk |
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