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PU5558: MACHINE LEARNING FOR HEALTHCARE (2022-2023)

Last modified: 08 Jul 2022 13:50


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
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Moses Ikpeme

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?

No

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 assessment, may be subject to change until 31 July 2022 for 1st half-session courses and 23 December 2022 for 2nd half-session courses.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 1 Computer Practical during University weeks 28 - 34
  • 1 Seminar during University weeks 26 - 35, 39

More Information about Week Numbers


Details, including assessment, may be subject to change until 31 July 2022 for 1st half-session courses and 23 December 2022 for 2nd half-session courses.

Summative Assessments

Report: Individual

Assessment Type Summative Weighting 30
Assessment Weeks 35 Feedback Weeks 38

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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 LevelThinking SkillOutcome
ProceduralAnalyseRelate a range of healthcare problems to appropriate machine learning algorithms
ProceduralApplyApply machine learning methods using R to address healthcare problems

Essay

Assessment Type Summative Weighting 70
Assessment Weeks 39 Feedback Weeks 42

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Feedback

An essay critically evaluating a published application of machine learning in healthcare

Word Count 3000
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualEvaluateDiscuss current challenges with implementing machine learning in healthcare
ProceduralAnalyseRelate a range of healthcare problems to appropriate machine learning algorithms
ProceduralUnderstandDescribe the machine learning workflow
ProceduralUnderstandExplain how machine learning is used 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

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Feedback

A report that addresses all machine learning aspects covered in the course.

Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ConceptualEvaluateDiscuss current challenges with implementing machine learning in healthcare
ProceduralApplyApply machine learning methods using R to address healthcare problems
ProceduralUnderstandExplain how machine learning is used to address healthcare problems
ProceduralAnalyseRelate a range of healthcare problems to appropriate machine learning algorithms
ProceduralUnderstandDescribe the machine learning workflow

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