Machine Learning Areas
Depending on the nature of the learning "signal" or "feedback" available to a learning system, machine learning tasks are typically classified into three broad areas:
Supervised learning is the type of learning that takes place when the training data are labelled with the correct outcome, which gives the learning algorithm examples for learning. This is like having a supervisor who can show different objects and tell what they represent. The task of the learning algorithm is to learn the relation. More technically, given a set of example inputs X and their outcomes Y, the supervised learning aims to learn a general mapping function f that transforms inputs to outputs: f: X a Y
On the other hand, unsupervised learning is harder because there is no supervisor telling you what the objects represent; instead, the learning algorithm should figure that out which objects go together by itself. Unsupervised learning algorithms do not assume any outcome labels Y, since they focus on grouping similar inputs X into clusters. Unsupervised learning can hence discover hidden patterns in data as well as similar items in the dataset.
Reinforcement learning assumes that an agent, which can be a robot, a bot or a computer program, interacts with a dynamic environment to achieve a specific goal. The environment is described with a set of states and the agent can take different actions to move from one state to another. Some states are marked as goal states and if the agent achieves that state, it receives a large reward. In other states, the reward is smaller, non-existing or even negative.