ML technical definition : Learning in AI defined

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Now that you have dived deep into the waters of machine learning, you might be interested in knowing how to define it. But before you can define machine learning, you need to learn what "learning" is. So, what is "learning" in Machine Learning?

Learning in AI defined comic scene

Learning in AI defined comic scene

Learning in ML and AI 

How do human beings learn? They improve with their experiences. Right!. This is exactly what learning is. But how would you define it, then?
Learning can be defined as the ability to improve with experience. In terms of AI and ML, it means making computer programs and systems which automatically improve with experience like human beings do. Experience can be thought of as "data".

In ML, we try to build models which learn from data to perform some task like decision-making, prediction, etc.

But this seems like an informal definition of ML. Right! How would you define it formally?

Definition of ML

The present standard definition of machine learning is given by Tom Mitchell, as follows:

 A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

 Here, you can think of experience E as the data given to the program. Task T could be prediction (classification, regression, etc.), decision-making, etc. The performance metric P could be accuracy (which you want to improve and optimize the program for) or some other metric (depends on the problem and your analysis of it). It basically means that the program is learning if it is improving its accuracy at predicting correctly with increasing available data (Generally speaking!). It is implicit that more data will lead to better results when it comes to learning. For example, if you train a model on 1 GB data and another on 2 GB data, then the model trained on 2 GB data should ideally perform better (although, it depends on a lot of other factors as well, like data quality, pre-processing, etc.)

With this, we have reached the end of this article and I hope the concept of learning is clear to you now. In the next article, I will discuss the applications and possible dangers of ML and AI. I might also discuss why some standard applications that are mentioned everywhere are just good to read in textbooks but have limited returns in the real world.

Stay tuned! Stay safe!

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