Learn to apply machine learning and AI techniques to data and discover how ethical frameworks can help you avoid teaching your machines bad habits. This course is essential for anyone needing a theoretical understanding of the opportunities and limitations of using machine learning on data. The course takes a practical approach to understand the key machine learning techniques, how they can be applied and what implications each has. Best of all, the course is designed to be non-technical. All practical exercises use drag and drop interfaces with virtual pens, post-its and paper.

The last two decades have seen dramatic advances in automation, from affordable smartphones that can understand your voice commands, to self-driving cars with safety records comparable to human drivers, and computers that can diagnose disease as well as experienced doctors. These advances have been driven not just by falling costs of computing power, but huge leaps forward in machine learning – techniques which automate the discovery of patterns and associations in data. The most powerful of these require minimal human expertise to guide that learning. In many cases, this means computers can discover the underlying rules and patterns in data by themselves. Whilst the terminology has exciting connotations in science fiction, artificial intelligence, or AI, is the use of these techniques to perform tasks that we previously thought could only be done by a human – driving a car, playing chess, or recommending medication, for example.

There's been plenty of discussion and debate on MBW's pages regarding the impact that Artificial Intelligence might have on the music business in the future. Obviously, there's its potentially seismic effect on the way musicians make music – whether that's AI producing non-human music from scratch, or providing tools that artists and songwriters can use to compose and perform in the studio. But there's also AI's application to more practical B2B tools to consider. Just last week, for example, we heard from Canada-based LANDR, which has launched an AI tool that helpfully sifts through its huge catalog of samples for those looking for a specific sound. Today, (September 4), a new twist on AI arrives via a fresh partnership between production music library Audio Network and Singapore-based machine learning company, Musiio.

Keen Footwear sells its iconic boots, shoes and sandals through thousands of retailers worldwide. But the Oregon manufacturer, which is working hard to honor its commitment to become "American Built," does not have the manpower to support a dedicated information security staff. With a team of six information technology professionals -- all but two focused on handling the day-to-day client issues of its 450 employees -- the IT staff would fall behind in triaging incidents the company's security software flagged. "We fit squarely in the realm that we have the problems of all the big players, but we don't have the resources of a large enterprise," said Clark Flannery, Keen's director of IT in Portland. To solve the problem, Flannery augmented his IT staff with machines.