Machine Learning

Machine learning: what type of learning does it use?

Several learning methods are common  in machine learning. The algorithms to train models fall into one of the following categories.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Semi_Supervised Learning

It is better to use an example to explain each category. Let’s consider the experience of a young child exploring his world for the first time. A child’s learning process is similar to that of a machine. Let’s say we want to teach our child the difference between an orange and an apple. In supervised learning, inputs and outputs are clearly defined, and an observer provides data and information (labels) to the learner. So we show each fruit to the child and repeat its name. Unlike supervised learning, which uses specific data, unsupervised learning does not require labels, and the learner must look at a particular pattern in the data to learn categories. So in an unsupervised learning example, we put a number of apples and oranges in front of the child without him knowing their difference beforehand and ask him to sort them into two separate categories and monitor his process. Reinforcement learning is based on rewards and punishments. In this learning method when an action is performed correctly, the model gets a reward and gets punished for incorrect actions with a reduced score. In this type of learning, the model attempts to increase its scores, which indicates that the model is learning correctly. In the case of a child, the parent shows the child an apple and asks him to identify it. A reward should be given to the child if he correctly identifies the fruit, and a warning if he incorrectly identifies it. As this process continues, the child becomes more adept at identifying fruits. Semi-supervised learning is a combination of supervised and unsupervised learning methods.