UPDATE (15.3.20): Given recent events, we are planning to run this as a remote event. Our subsequent course will be in June, and we will make a decision regarding an in-person vs remote session then. Those with a strong preference for an in-person event may wish to consider waiting until then. We are happy to transfer tickets between events or issue refunds on request.

Join us for a weekend course covering the fundamental theories of machine learning, the principles of applying it to healthcare and how to do so in practice.

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.

The first day will focus on understanding machine learning and how it is currently being applied. The second day will focus on working to apply the technology to healthcare problems, including the principles of product management applied to clinical AI solutions.

Note: Although there is complementarity between the two days, attendees are free to attend either as standalone courses.

DAY ONE: Understanding Machine Learning for Healthcare

We 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 webinar: https://www.youtube.com/watch?v=leDBwoaWQ6E

See also several blog posts, including a machine learning in medicine careers guide, at www.chrislovejoy.me/ml-medics

DAY TWO: AI Product Management Fundamentals - Application in Healthcare

We will cover the fundamentals of product management and how these can be applied into clinical AI products:

• What is product management?
• Product design principles, user research and design frameworks
• Product vision, product strategy and key metrics
• Agile product development
• What is an AI product?
• What are the different AI products in health care?
• Train an AI to diagnose CXR without writing any code

As the number of AI digital health startups grow, there is an increasing awareness of the need to get clinicians' input when developing AI products for patients and doctors. If you are considering moving into the AI digital health space, this is a course where you could learn the basics of how clinicians could help shape AI products by providing their clinical expertise while understanding the basic concepts of product management. The course is also suitable for people without a healthcare background who are looking to learn more about digital health products.