James Weaver is a Java developer, author, and speaker with a passion for helping Java to be increasingly leveraged in cloud-native and machine learning applications. He is a Java Champion, and a JavaOne Rockstar. James has written books including Inside Java, Beginning J2EE, the Pro JavaFX series, and Java with Raspberry Pi. As an Pivotal Developer Advocate, James speaks internationally at software technology conferences about Java and Cloud Native development. James tweets as @JavaFXpert, blogs at http://JavaFXpert.com and http://CulturedEar.com and may be reached at jweaver [at] pivotal.io
In the age of quantum computing, computer chip implants and artificial intelligence, it’s easy to feel left behind. For example, the term "machine learning" is increasingly bandied about in corporate settings and cocktail parties, but what is it, really?
In this session, James Weaver and Katharine Beaumont will give a gentle introduction to machine learning topics such as supervised learning, unsupervised learning, reinforcement learning, and deep learning. We'll also survey various machine learning APIs and platforms. We’ll give you an overview of what you can achieve, as well as an intuition on the maths behind machine learning.
The presenters are very aware that some material on machine learning can be maths-intensive, and off-putting if you are not confident with your calculus. Conversely, some material doesn’t go into enough detail so you don’t get a feel for how things actually work. We aim to give the session we wish we’d attended at the start of our journey: We will start right at the beginning with the basics, and build up in an approachable way to some of the most interesting techniques so you can get the most out of your machine learning adventure.