Machine learning is not a new subject; it has existed in academia for well over 70 years as a formal discipline, but known by different names: statistics, and more generally mathematics, then artificial intelligence (AI), and today as machine learning. While the other related subject areas of statistics and AI are just as prevalent, machine learning has carved out a separate niche and become an independent discipline in and of itself.

In simple terms, machine learning involves predicting future events based on historical data. We see it manifested in our day-to-day lives and indeed we employ, knowingly or otherwise, principles of machine learning on a daily basis.

When we casually comment on whether a movie will succeed at the box office using our understanding of the popularity of the individuals in the lead roles, we are applying machine learning, albeit subconsciously. Our understanding of the characters in the lead roles has been shaped over years of watching movies where they appeared. And, when we make a determination of the success of a future movie featuring the same person, we are using historical information to make an assessment.

As another example, if we had data on temperature, humidity, and precipitation (rain) over a period of say, 12 months, can we use that information to predict whether it will rain today, given information on temperature and humidity?

This is akin to common regression problems found in statistics. But, machine learning involves applying a much higher level of rigor to the exercise to reach a conclusive decision based not only on theoretical calculations, but verification of the calculations hundreds or thousands of times using iterative methods before reaching a conclusion.

It should be noted and clarified here that the term machine learning relates to algorithms or programs that are executed typically on a computing device whose objective it is to predict outcomes. The algorithms build mathematical models that can then be used to make predictions. It is a common misconception that machine learning quite literally refers to a machine that is learning. The actual implication, as just explained, is much less dramatic.