What is Learning? — An Example of Linear Classification

Problem. Suppose that we are a bank trying to learn the creditworthiness of our current customers. Our resources include a database of the history of previous customers who have been already classified as defaulters or not. Based on this history, we are to learn the function $f$ which takes a customer as input and spits out the (binary) value of their creditworthiness ("yes" or "no").

[Assume that the customers are linearly separable.]

What is Learning?

To put it in simple words, learning is the process by which an entity gains the ability to predict outcomes in an unknown domain by experiencing or observing patterns in a known domain of data. Learning means to study the features of a given sample of data and come to an inductive generalization or extrapolation of such characteristics for the entire population of which the sample is merely a part. For example, you observe that the sun rises in the east and sets in the west today and tomorrow and the days after that every day for a year. By the end of the year, if you predict that the sun will rise again in the east and set in the west—correctly so—then you have learnt.