Rule-based machine learning is a way of teaching computers certain rules that help them decide what to do in certain situations. It works by feeding the computer lots of examples of a certain type of situation. Each example has a label telling the computer what should be done in that situation. After learning all these rules, the computer can then use them to figure out what to do when it sees a new situation. For example, if you were teaching a computer how to recognize objects in an image, you would feed it lots of images with labels of the objects it should recognize. After learning all the rules, the computer would then be able to recognize new objects it has not seen before.