Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a “reasonable” way (see inductive bias).
Supervised Learning explained by Georgia Tech researchers on Machine Learning.
The parallel task in human and animal psychology is often referred to as concept learning.
Supervised learning is one of the three main areas in machine learning:
Books on Supervised Learning
Here are a couple of books related to supervised machine learning on Amazon.