PU5558: Machine Learning for Healthcare

PU5558: MACHINE LEARNING FOR HEALTHCARE (2024-2025)

Last modified: 20 Jun 2025 15:06

Course Overview

Machine learning has the potential to revolutionise healthcare. The aim of this course is to introduce machine learning for health data science with examples of real-life healthcare applications, using the popular data science language R.

Course Details

Study Type	Postgraduate	Level	5
Term	Second Term	Credit Points	15 credits (7.5 ECTS credits)
Campus	Aberdeen	Sustained Study	No
Co-ordinators	Dr Dimitra Blana

What courses & programmes must have been taken before this course?

Any Postgraduate Programme

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

Course Description

This introductory course will give students from a variety of backgrounds a firm understanding of machine learning and its application to the health domain. The course will cover the foundations of machine learning; case studies of machine learning applications using health data; technical, ethical and legal challenges in the field; active areas of research in machine learning; and the machine learning workflow using R (no coding experience is required).

Details, including assessments, may be subject to change until 21st September 2026 for Term 1 and Full Year courses and 8th January 2027 for Term 2 courses.

Summative Assessments

Essay

Assessment Type	Summative	Weighting	70
Assessment Weeks	39	Feedback Weeks	42	Look up Week Numbers
Feedback	An essay critically evaluating a published application of machine learning in healthcare	Word Count	3000

Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Conceptual	Evaluate	Discuss current challenges with implementing machine learning in healthcare
Procedural	Analyse	Relate a range of healthcare problems to appropriate machine learning algorithms
Procedural	Understand	Explain how machine learning is used to address healthcare problems
Procedural	Understand	Describe the machine learning workflow

Report: Individual

Assessment Type	Summative	Weighting	30
Assessment Weeks	35	Feedback Weeks	38	Look up Week Numbers
Feedback	A report explaining the choice of appropriate machine learning algorithms for example health care problems, and their implementation using R. describing the application of a suitable machine learning algorithm to address an example healthcare problem

Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Analyse	Relate a range of healthcare problems to appropriate machine learning algorithms
Procedural	Apply	Apply machine learning methods using R to address healthcare problems

Formative Assessment

There are no assessments for this course.

Resit Assessments

Report: Individual

Assessment Type	Summative	Weighting	100
Assessment Weeks	43	Feedback Weeks	46	Look up Week Numbers
Feedback	A report that addresses all machine learning aspects covered in the course.

Learning Outcomes

Sorry, we don't have this information available just now. Please check the course guide on or with the Course Coordinator
Knowledge Level	Thinking Skill	Outcome

Course Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Understand	Explain how machine learning is used to address healthcare problems
Conceptual	Evaluate	Discuss current challenges with implementing machine learning in healthcare
Procedural	Apply	Apply machine learning methods using R to address healthcare problems
Procedural	Analyse	Relate a range of healthcare problems to appropriate machine learning algorithms
Procedural	Understand	Describe the machine learning workflow

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