Farr Institute London Short Courses


Using Machine Learning in Health Research

In an era of modern healthcare, it is essential that all stakeholders are aware of the foundations of machine learning and the latest trends in this field.

This introductory and interactive course will provide participants with clear insights into the associated challenges and opportunities.

This one-day course will cover the basic aspects of machine learning in healthcare. In association with interactive lecture sessions, a number of practical and group discussions are included to make for a vibrant and engaging course. This serves as an opportunity to explore the concepts in greater depth, raise questions, and enable participants to acquire greater understanding regarding the role of Machine Learning in healthcare to automatically discover new associations and the construction of clinical rules and predictive models.

Learning Objectives

By the end of this course, participants should be able to:

  • Outline the requirements for applying machine learning approaches to healthcare data and assess when their application is warranted;
  • Describe methods for the selection and extraction of relevant features;
  • Investigate and define suitable machine learning methods for problems in prevention, diagnosis, prognosis, phenotyping, and therapy;
  • Design effective evaluation frameworks;
  • Contrast the strengths and weaknesses of various machine learning methods.

Planned Timetable

Time Session Title Led by
09:00-09:30 Registration and coffee  

  • Machine learning for therapy, diagnosis, prevention, and prognosis
  • Probabilistic methods
Dr Dionisio Acosta
11:00-11:15 Coffee  

  • Support vector machines
  • Neural networks
  • Hyper-parameter tuning and model evaluation
Dr Holger Kunz
12:45-13:30 Lunch  

  • Decision trees
  • Ensembles
  • Data pre-processing and dimensionality reduction
Dr Dionisio Acosta
15:00-15:15 Coffee  
  • Applied machine learning on health datasets
Dr Holger Kunz 

Course Team

Dr Dionisio Acosta (Lead Tutor)
Dr Holger Kunz (Lead Tutor)