In this piece, I want to look at two other concepts which are vital to understanding how machines are becoming increasingly smarter and able to perform tasks which previously could only be done by humans.

Supervised and unsupervised learning describe two ways in which machines – algorithms – can be set loose on a data set and expected to learn something useful from it.

Today, supervised machine learning is by far the more common across a wide range of industry use cases. The fundamental difference is that with supervised learning, the output of your algorithm is already known – just like when a student is learning from an instructor. All that needs to be done is work out the process necessary to get from your input, to your output. This is usually the case when an algorithm is being “taught” from a training data set. If the algorithms are coming up with results which are widely different from those which the training data says should be expected, the instructor can step in to guide them back to the right path.

Unsupervised machine learning is a more complex process which has been put to use in a far smaller number of applications so far. But this is where a lot of the excitement over the future of AI stems from. When people talk about computers learning to “teach themselves”, rather than us having to teach them (one of the principles of machine learning), they are often alluding to unsupervised learning processes.

Bernard Marr → Supervised V Unsupervised Machine Learning -- What's The Difference?