(Saturday) 9:00 am - 4:30 pm
To Be Determined In Central London London United Kingdom
This one-day course covers the core principles of machine learning and its application in healthcare.
This course is open to both medical professionals (doctors, medical students, nurses and allied healthcare professionals) with an interest in machine learning, as well people from other professions (such as data scientists) looking to understand it’s applications in medicine.
There will be an event run specifically for medical students on 30th November, which medical students may wish to attend: ‘Machine Learning for Medical Students’. For tickets, message firstname.lastname@example.org or contact your local MedSoc or MedTech society.
The course will cover:
- What exactly is machine learning and how might it be useful in medicine?
- Methods for learning
- Core concepts: supervised vs unsupervised machine learning, gradient descent, overfitting and underfitting, performance measures
- The role of linear and logistic regression in medical models, and their augmentation with machine learning
- What are neural networks and how do they work?
- Convolutional neural networks (CNNs), diagnostic imaging and other medical applications
- What is transfer learning and what is it’s relevance in healthcare?
- Recurrent neural networks (RNNs), natural language processing (NLP) and other medical applications
- Alternative methods of regression and classification
- Critical appraisals of current research and case studies, and what we can learn from them
- Careers advice for medical professionals looking to combine machine learning with medicine
- Recommendations for next-step resources
The course is designed to be accessible for someone with no previous background in machine learning while still being useful to those who have had some exposure.
The course deliberately avoids going to the level of technicality that alternative machine learning courses do, while tailoring all examples and discussion to applications within healthcare. The focus will be on principles rather than the underlying mathematics; very few calculations will be performed. The course aims to provide the depth required to understand, appraise and become involved with healthcare AI research and enterprise.
For a taster, see a recording of our free webinar: https://www.youtube.com/watch?v=leDBwoaWQ6E
See also several blog posts, including a machine learning in medicine careers guide, at chrislovejoy.me/ml-medics
“Very condensed and informative. Explained details which an average medical graduate could understand. Inspiring to hear how Chris got involved in ML and currently doing a masters on the topic.”
“Great delivery. Your personal experiences. Clarity in your messages. Concise which is great for beginners.”
“Broad scope, thoughtful planning, informed editing ie relevant and useful”