The terms ‘big data’, ‘AI’ and ‘machine learning’ are often used interchangeably but there are subtle differences between them.

A subfield of artificial intelligence (AI), machine learning (ML) is broadly defined as the capability of a machine to imitate intelligent human behavior.

ML algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. Put differently, it’s “…the set of techniques and tools that allow computers to ‘think’ by creating mathematical algorithms based on accumulated data.” (iQ, Intel’s tech culture magazine).

The UK Information Commissioner ‘s Office (ICO) categorised ML into two types of learning

• Supervised learning where algorithms are developed based on labelled datasets (i.e., developed predictive models based only on input data).

• Unsupervised learning where the algorithms are not trained and are instead left to find regularities in input data without any instructions as to what to look for (i.e., group and interpret data based only on input data).

The ability of the algorithms to change their output based on experience is what gives ML its power.

With the rise in big data, ML has become one of the key AI techniques for solving problems in areas, such as computational finance and algorithmic trading but have some implications when it comes to data protection. Indeed, because ML is a complex method of big data analysis, it can make it a challenge for organisations to be transparent about the processing of personal data.