Katharine is a Java developer, having worked on medical software, Big Data and complex event processing, web development and machine learning. She has given a number of conference talks on her experiences in the software industry. With a background in law, science, mathematics and more recently machine learning, she (unsurprisingly) loves learning! She works as the Community & Content Manager for Voxxed, where her goal is to demystify complicated concepts and help spread knowledge among the developer community. On the side, she enjoys playing with Matlab/Python/Ruby, cycling, walking her dogs and living on a farm.
Deep in study, and drowning in flow charts, a well-meaning friend interrupted and asked if I wanted a cup of tea. I blinked in the light of social interaction, mentally traversing notes, cue cards and flow charts. I was confused. “I don’t have a diagram for that”.
Tales of how programming and study have threatened social interactions.
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.
Who should attend your session?
Anyone with an interest in learning more about Machine Learning - and any level.
What are the 'next steps' for an attendee to take after attending your session?
Definitely start looking into some Machine Learning libraries like DeepLearning4J, or take a look at some of the big Machine Learning APIs if you want to get going immediately. Otherwise, if you want more grounding in the theory, go to Coursera and take Andrew Ng’s course from Stanford Online.
Who is your favourite British fictional character?
What is the most interesting thing you've discovered about Machine Learning/AI?
Word vectors and how, only with context, Neural Networks can discover semantic similarities and illustrate in vector space.
What are the advantages of studying Machine Learning?
Along with the IoT revolution in cities and industry, it’s the direction the most exciting technology is going in.