Machine Learning in Healthcare
Step into the world of Machine Learning for Healthcare with Western Sydney University’s Centre for Research in Mathematics (opens in a new window) and learn first-hand how this methodology can be applied to improve clinician and patient outcomes.
About the class
The field of Machine Learning is almost 70 years old but only in recent years has become a well-known topic outside of science fiction. With commercial successes in social media, computer games, finance, and advertising, Machine Learning now plays an increasingly important role in medicine and health care. For instance, it already is involved in inspecting and classifying medical images with high accuracy; analysing clinical measurements and detecting anomalies; and better predicting hospital length-of-stay. And we can expect this involvement to grow. Advanced algorithms and data are the main ingredients of modern machine learning, and understanding their principles is key to harnessing the full potential of machine learning in the healthcare sector.
In this 3 hour workshop participants will be introduced to machine learning methodology by working through concrete examples of its application to diagnosis and detection.
Through real world case studies participants will:
- Understand the fundamentals, applications and limits of Machine Learning;
- Walk through the problem-solving process using the Machine Learning methodology; and
- Identify opportunities for Machine Learning in a healthcare environment.
Participants will be invited to bring potential problems from their work that may be amenable to machine learning, and a selection of these will be work shopped as model problems.
Standard registration - $350
Alumni - $300
Date - Thursday 26th September 2019
Time – 1pm-4pm
Location - Western Sydney University - Liverpool Campus Room 03.4.02
Register via Onestop (opens in a new window) by 24th September 2019
Limited seats - Maximum 20 participants per workshop.
For more information please contact Judy Foster via email or call 02 9678 7419.