Machine Learning and Statistics for Health (L7) (974G1)

15 credits, Level 7 (Masters)

Spring teaching

This module is designed to provide you 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 
• clustering. 

This will help you develop a diverse skill set to navigate the intricacies of medical data analysis effectively. Teaching will blend theory and hands-on methods, and reinforce practical skills. 

Including supervised and unsupervised learning techniques, such as logistic regression and medical data clustering, this module equips you with the expertise needed to excel in the intricate world of health and medical data analytics. On completion, you’ll confidently tackle real-world healthcare problems using data-driven insights.

 

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 33 hours of contact time and about 117 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2024/25. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum. We’ll make sure to let you know of any material changes to modules at the earliest opportunity.

Courses

This module is offered on the following courses:

• Mathematics (research placement) MMath
• Mathematics MMath
• Mathematics with Data Science MMath
• Mathematics with Economics MMath
• Mathematics with Finance MMath