Machine Learning & Stats for Health L6 (G5221)
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Machine Learning and Statistics for Health (L6)
Module G5221
Module details for 2025/26.
15 credits
FHEQ Level 6
Module Outline
This module is designed to provide students with a comprehensive understanding of analytical and interpretative statistical methods and tools essential for solving complex problems in the fields of health and medicine. The module is structured around three overarching areas:
• survival analysis,
• classification, and
• clustering,
offering a diverse skill set that enables students to navigate the intricacies of medical data analysis effectively. The lectures will blend theory and hands-on methods, with R labs run to reinforce practical skills.
Including supervised and unsupervised learning techniques, such as logistic regression and medical data clustering, this module equips students with the expertise needed to excel in the intricate world of health and medical data analytics. Upon completion, students will confidently tackle real-world healthcare problems using data-driven insights.
Module learning outcomes
Systematic understanding of and proficiency in the theory and methods of medical statistics and machine learning techniques
Apply advanced techniques such as non-linear models and clustering to analyse real-world health and medical data
Apply and interpret results of survival analysis, and statistical learning techniques applied to medical problems
Use statistical software to enhance a practical understanding of theory application in real-world contexts.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 20.00% | |
| Coursework components. Equal weighting for all components. | ||
| Project | T2 Week 9 | |
| Portfolio | T2 Week 11 | |
| Unseen Examination | Semester 2 Assessment | 80.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Lecture | 1 hour | 10000000111 |
| Spring Semester | Lecture | 2 hours | 11111111111 |
| Spring Semester | Practical | 1 hour | 01111111000 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr Marianna Cerasuolo
Assess convenor, Convenor
/profiles/612334
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