I've recently been speaking at developer conferences about machine learning, with my primary focus being to shed light on some of its seemingly magical inner workings. I'd like to share the slides from a recent session:
Also, here is a video of a presentation on the subject that I did with Sandhya Kapoor of IBM at JFall in Netherlands:
Regarding future sessions, I'm really looking forward to giving a Machine Learning Exposed workshop with Katharine Beaumont at Devoxx/UK on 11 May 2017. Here's the abstract in an attempt to pique your interest:
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 explore 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.
Please join us if you can!
James L. (Jim) Weaver