What is machine learning?

Machine Learning (ML) is an interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It focuses on how computers can simulate or realize human learning behavior, so as to acquire new knowledge or skills, and reorganize the existing knowledge structure to continuously improve their own performance.

It is the core of artificial intelligence and the fundamental way to make computers intelligent. Its application covers all fields of artificial intelligence, and it mainly uses induction, synthesis rather than deduction.

Basic introduction:

Machine learning is a multidisciplinary subject that has emerged in recent 20 years, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and many other disciplines. Machine learning theory is mainly to design and analyze some algorithms that make computers "learn" automatically. Machine learning algorithm is an algorithm that automatically analyzes and obtains rules from data and uses the rules to predict unknown data. Because learning algorithms involve a large number of statistical theories, machine learning is closely related to statistical inference, which is also called statistical learning theory. In algorithm design, machine learning theory focuses on realizable and effective learning algorithms. Many reasoning problems are difficult to follow, so part of machine learning research is to develop approximate algorithms that are easy to handle.

Machine learning has been widely used, such as data mining, computer vision, natural language processing, biometric identification, search engine, medical diagnosis, credit card fraud detection, securities market analysis, DNA sequencing, voice and handwriting recognition, strategic games and robot applications.

Learning is an important intelligent behavior of human beings, but what is learning has been debated endlessly. Sociologists, logicians and psychologists all have different views. Such as Langley (1996)? The definition of machine learning is "machine learning is a science of artificial intelligence, and the main research object in this field is artificial intelligence, especially how to improve the performance of specific algorithms in empirical learning". Machine learning is an artificial science. The main research object in this field is artificial products, that is, specific algorithms to improve their performance through experience. Tom Mitchell's Machine Learning (1997) explains some concepts in information theory in detail, in which machine learning is defined as "the study of computer algorithms that can be automatically improved through experience". Machine learning is the study of computer algorithms that are automatically improved through experience. Alpaydin (2004) also put forward his own definition of machine learning. "Machine learning is to optimize the performance standards of computer programs by using data or past experience." Machine learning is programming a computer to optimize performance standards using sample data or past experience. )

Nevertheless, in order to discuss and estimate the progress of this subject, it is necessary to give a definition of machine learning, even if this definition is incomplete and insufficient. As the name implies, machine learning is a subject that studies how to use machines to simulate human learning activities. Strictly speaking, machine learning is the study that machines acquire new knowledge and skills and identify existing knowledge. The "machine" mentioned here refers to the computer; Now it is an electronic computer, and in the future it may be a neutron computer, a photon computer and a neural computer.

Can machines learn like humans? 1959 Samuel of the United States designed a chess program, which has the ability to learn and can improve his chess skills through continuous playing. Four years later, the program beat the designer himself. Three years later, the program defeated an unbeaten champion in the United States who had won for eight years. This program shows people the ability of machine learning and raises many thought-provoking social and philosophical questions.

One of the main arguments of many people who have negative opinions on whether the machine can surpass human beings is that the machine is man-made, and its performance and action are completely stipulated by the designer, so its ability will not surpass the designer himself in any case. This opinion is true for machines without learning ability, but it is worth considering for machines with learning ability, because the ability of such machines is constantly improving in application. After a period of time, the designer himself doesn't know what level his ability has reached.

Machine learning is a relatively young branch of artificial intelligence research, and its development process can be roughly divided into four periods.

The first stage is from the mid-1950s to the mid-1960s, which is a warm period. …& gt；

The second stage is from the mid-1960s to the mid-1970s, which is called the cooling-off period of machine learning.

The third stage is from the mid-1970s to the mid-1980s, which is called the renaissance period.

The latest stage of machine learning begins at 1986.

The important performance of machine learning entering a new stage is as follows:

(1) Machine learning has become a new frontier discipline and a course in colleges and universities. It integrates applied psychology, biology and neurophysiology as well as mathematics, automation and computer science, and forms the theoretical basis of machine learning.

(2) Various forms of comprehensive learning systems are emerging, which combine various learning methods and learn from each other. In particular, the coupling of connected learning symbol learning can better solve the problem of acquiring and refining knowledge and skills in continuous signal processing, which has been paid attention to.

(3) A unified view of various basic problems of machine learning and artificial intelligence is taking shape. For example, the idea that learning is combined with problem solving, and knowledge expression is simple and easy to learn, has produced chunk learning of SOAR, a general intelligent system. Case teaching method, which combines analogy learning with problem solving, has become an important direction of experiential learning.

(4) The application scope of various learning methods is expanding, and some of them have become commodities. The knowledge acquisition tool of inductive learning has been widely used in diagnostic classification expert system. Connecting learning plays a dominant role in audio-visual recognition. Analytical learning has been used to design a comprehensive expert system. Genetic algorithm and reinforcement learning have a good application prospect in engineering control. Neural network connection learning coupled with symbolic system will play a role in intelligent management of enterprises and motion planning of intelligent robots.

(5) Academic activities related to machine learning are unprecedentedly active. In addition to the annual machine learning seminar, there are also international computer learning theory conferences and genetic algorithm conferences.