Overview of Machine Learning

In layman's terms, machine learning is to allow computers to learn automatically from data to obtain certain knowledge. As a discipline, machine learning usually refers to a type of problem and the method to solve this type of problem, that is, how to find the law from the observation data, and use the learned law to predict the unknown or unobservable data. In the early engineering field, machine learning is often called pattern recognition, but pattern recognition is more biased towards specific application tasks, such as optical character recognition, speech recognition, and face recognition. The characteristic of these tasks is that for us humans, these tasks are easy to complete, but we do not know how we do it, so it is difficult to manually design a computer program to complete these tasks. A feasible method is to design an algorithm that allows the computer to learn the rules from the labeled samples and use it to complete various recognition tasks. With the increasing application of machine learning technology, the concept of machine learning is now gradually replacing pattern recognition, becoming the general term for this type of problem and its solutions. Taking handwritten digit recognition as an example, we need to allow the computer to automatically recognize handwritten digits. Handwritten digit recognition is a classic machine learning task, which is simple for humans, but very difficult for computers. It is difficult for us to summarize the handwriting characteristics of each digit, or the rules for distinguishing different digits, so designing a set of recognition algorithms is an almost impossible task. In real life, many problems are similar to those of handwritten number recognition, such as object recognition and speech recognition. For this kind of problem, we don't know how to design a computer program to solve it. Even if it can be realized by some heuristic rules, the process is extremely complicated. Therefore, people began to try another way of thinking, that is, let the computer see a large number of samples, and learn some experience from them, and then use these experiences to identify new samples. To recognize handwritten digits, first manually annotate a large number of handwritten digital images (that is, each image is manually marked with what number it is), these images are used as training data, and then a set of models are automatically generated through the learning algorithm, and rely on it. This method of learning through data is called the method of machine learning. First, we use a life example to introduce some basic concepts in machine learning: samples, features, labels, models, learning algorithms, etc. Suppose we want to buy mangoes in the market, but we have no previous experience in selecting mangoes, how can we obtain this knowledge through learning? First, we randomly select some mangoes from the market and list the characteristics of each mango.