Waterford Tech Meetup celebrated its third birthday recently, providing an opportunity for the tech community in Waterford and surrounding areas to meet in a friendly environment. Meetups happen monthly on Wednesday evenings in the Boxworks co-working space on Patrick Street in Waterford. Each meetup includes one or more presentations, followed by an opportunity to chat, mix and mingle with peers over slices of pizza. Everyone is welcome, but attendees generally are drawn from undergraduate students, developers, designers and entrepreneurs.
The session was called “Intro to Machine Learning & Building Engaging Chatbots”. It was chaired by Marco Troisi of Servisbot. I had been asked to present an introduction to machine learning, suited to the tech meetup audience. Therefore, the talk presented machine learning as a set of procedures for gaining insights from data, integrated into larger applications. The topic clearly was attractiuve to Meetup members, because 105 members registered to attend, and all seats were taken.
After introducing the topic, I described some of the best known and/or most mature applications in machine learning, with examples from the finance, health care, government, retail, industry 4.0 and transportation sectors. The talk described some categories of machine learning algorithms and how they can be invoked in practice. It described some practical steps to take to avoid common problems, before describing some options for gaining the expertise needed to use machine learning with confidence. Of course, software engoineers have complementary skills (see figure below). The talk concluded with advice for how the “two cultures” of machine learning and software engineering could draw upon their complementary strengths in applications ready for deployment in production.
The Two Cultures: contrasting the world of machine learning and software engineering
Many of the questioners after the talk asked for more information about machine learning, particularly educational resources and qualifications. I mentioned the Data Mining modules taught in WIT. Clearly there is a great demand from the tech community in this region for information on how machine learning can contribute in their workplaces.
Written by Bernard Butler