2nd November 2019, 09:00AM - 4:30PM
Location: London, UK
This one-day course covers the core principles of machine learning and its application in healthcare.
It is aimed at all medical professionals (doctors, medical students, nurses and allied healthcare professionals) with an interest in machine learning but who have not undergone a formal education in the area.
The course will cover:
- What exactly is machine learning and how might it be useful in medicine?
- 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
- Non-neural network 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.