Duration: 1 day

Come and learn about the essential mechanics of machines that learn from experience.

Instead of a focus on algebra and calculus, this course explains the design principles that have been brought together to make thinking machines. The context for this recent success is the many failures from before – and these are outlined very briefly before taking a deeper dive into the mechanisms behind learning, the subtle but powerful properties expressed through neural computation, and the simple design but extraordinary characteristics of the transformer architecture.

This course takes a layered approach which begins by setting the historical and biological context to artificial intelligence. Next is a canonical outline that brings together the most important ideas. We finish by looking inside an example codebase used to construct and train a model, and then the physical manifestation of a model itself. This final session is a much more hands-on and interactive session and it will help if you can follow along on your own computer. There is an instructor available to help guide you.

The benefit of this course is that an expert is available for you to ask any questions you might have about machine learning in general, which we will do our best to answer – so the real value is that it is an interactive presentation, that draws on our many years of experience – and is designed for you to explore as you learn.

There is no heavy theory here – and no high expectations of coding prowess – just a crystal clear explanation of the most powerful ideas and critical concepts that led to the emergence of AI – in a few clear stages. This course uses the same language and framework for thought as used the experts, but it is open to anyone with a basic level of curiosity and interest in the foundations of AI.

Think of this as the on-ramp to expertise in artificial intelligence, but designed for people who need to build strategy around artificial intelligence and innovation – not necessarily to build it themselves

Courses

Learning Machines

Learning Machines