What is AI learning? What is the main thing to learn in artificial intelligence?

Artificial Intelligence: Artificial Intelligence, Society and Humanities, Philosophical Foundations and Ethics of Artificial Intelligence, Advanced Robot Control, Cognitive Robots, Robot Planning and Learning, Bionic Robots, Swarm Intelligence and Autonomous Systems, Unmanned Technology and System Implementation, Game Design and Development, Computer Graphics, Virtual Reality and Augmented Reality, Modern Methods of Artificial Intelligence I, Problem Expression and Solving, Modern Methods of Artificial Intelligence II, Machine Learning, Natural Language Processing, Computer Vision, etc.

At present, the learning content of artificial intelligence major mainly includes: machine learning, introduction to artificial intelligence (search method, etc.), biological evolution theory, image recognition, natural language processing, semantic web, game theory, etc. The main prerequisite courses required are:

Signal processing, linear algebra, calculus, programming (preferably with a foundation in data structures), etc.

Artificial intelligence is an interdisciplinary discipline, in which mathematics and computer programming are the two most important aspects of learning artificial intelligence. These articles "Understanding Artificial Intelligence" before the "Zhiyun AI Column" have also been introduced to you, and students who have not read it can take a look. For engineers who have graduated, it is generally necessary to supplement some knowledge of mathematics or programming before learning AI systematically.

From a broad technical perspective, the knowledge system of artificial intelligence mainly involves six major learning directions, including natural language processing, computer vision, machine learning (deep learning), automatic reasoning, knowledge representation and robotics, which are systematically and closely related.

Experts in the field of artificial intelligence have made different definitions of artificial intelligence, but there is no unified version of artificial intelligence at present, and their definitions have some common rights that can be summarized as: the hardware that depends on the computer (note: this computer does not only refer to the laptops we use every day) has its own specific algorithms that can learn from experience to improve their own shortcomings.

The definition of artificial intelligence is divided into two parts, namely artificial and intelligent. Artificial is easier to understand and less controversial. Intelligence includes more problems, involving issues such as consciousness, self, thinking, and so on.

This consciousness and thinking includes the content of the question, which is the self-learning process of artificial intelligence.

The main courses of artificial intelligence learning are: "Artificial Intelligence, Society and Humanities", "Philosophical Foundations and Ethics of Artificial Intelligence", "Advanced Robot Control", "Cognitive Robots", "Robot Planning and Learning", "Bionic Robots", "Swarm Intelligence and Autonomous Systems", "Unmanned Technology and System Implementation", "Game Design and Development", "Computer Graphics", "Virtual Reality and Augmented Reality", "Modern Methods of Artificial Intelligence I", "Problem Expression and Solving", " Modern Methods of Artificial Intelligence II, Machine Learning, Natural Language Processing, Computer Vision.

Generally speaking, the artificial intelligence studied by graduate students is a bit practical for the society, the undergraduate science is theoretical, artificial intelligence is a comprehensive subject, high mathematics is absolutely indispensable, Internet-related courses need to be understood, as well as machines, machine control technology, etc.

At present, artificial intelligence has changed the industry by replacing some repetitive work with machines, but relatively speaking, machines can only do some repetitive work.

Experts in the field of artificial intelligence have different definitions of artificial intelligence.

First you need a foundation in mathematics: advanced mathematics, linear algebra, probability theory, mathematical statistics and stochastic processes, discrete mathematics, numerical analysis;

secondly, the accumulation of algorithms is needed: artificial neural networks, support vector machines, genetic algorithms and other algorithms;

Of course, there are also algorithms that are needed in various fields, for example, if you want to let the robot navigate and map the location environment by itself, you need to study SLAM;

Artificial Intelligence, Society and Humanities, Philosophical Foundations and Ethics of Artificial Intelligence, Advanced Robot Control, Cognitive Robotics, Robot Planning and Learning, Bionic Robots, Swarm Intelligence and Autonomous Systems, Unmanned Driving Technology and System Implementation, Game Design and Development, Computer Graphics, Virtual Reality and Augmented Reality, Modern Methods of Artificial Intelligence I, Problem Expression and Solving, Modern Methods of Artificial Intelligence II, Machine Learning, Natural Language Processing, Computer Vision.

Artificial Intelligence (AI) in English. It is a new technical science that researches and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can respond in a similar way to human intelligence, including robotics, language recognition, image recognition, natural language processing, and expert systems. Since the birth of artificial intelligence, the theory and technology have become increasingly mature, and the application field has been expanding. Artificial intelligence can simulate the information process of human consciousness and thinking.

Artificial intelligence is not human intelligence, but it can think like a human being, and may surpass human intelligence.

Artificial intelligence is an extremely challenging science, and those who do it must be computer literate, psychological, and philosophical. Artificial intelligence is a very broad science, it is composed of different fields, such as machine learning, computer vision, etc., in general, one of the main goals of AI research is to enable machines to do some complex tasks that usually require human intelligence to complete. But different times and different people have different understandings of this "complex work".

In December 2017, artificial intelligence was selected as one of the "Top Ten Popular Words in China in 2017".

1.From the basic disciplines of analysis, artificial intelligence mainly has to learn mathematics, computers, algorithms, psychology, statistics, and probability. Of course, these are mostly basic.

2.Directions of artificial intelligence, machine learning, deep learning, pattern recognition, computer vision, etc. No, myself.

3.Artificial intelligence has broad prospects Artificial intelligence has been included in the country's medium and long-term development plans. In the future, no, now artificial intelligence has been or is.

Artificial intelligence is very difficult to learn, and the main direction of artificial intelligence is deep learning, which involves a lot of mathematical content, and it is no longer a simple and pure programming problem. If you need to learn artificial intelligence technology, it is recommended to choose [Danai Education]. Ways to learn artificial intelligence:

1. Lay a good foundation and learn advanced mathematics and [python programming language]. Advanced mathematics is the basis for learning artificial intelligence, because artificial intelligence will design a lot of data and algorithms, and these algorithms are mathematically derived, so to understand algorithms, you need to learn some knowledge of higher mathematics first. First, students will learn the basics of advanced mathematics, starting with basic data analysis, linear algebra and matrices.

2. Advance to the stage and start learning machine learning algorithms. Once you have mastered the above basics, you need to start learning machine learning algorithms...

The learning of artificial intelligence, to put it simply, has 3 points, which is equivalent to learning artificial intelligence, and then looking for a job internship.

The first point is to learn math well.

Artificial intelligence is a branch of computer science, but there are times when it uses other computer technologies, and it is very similar to the main components of computers, and the difference is mainly in form. They are all hardware and software together, hardware is the actual visible and touchable objects, and software is running internally, is a kind of hardware control, to achieve "intelligent" program. Software is mainly done through programming.

Programming is a large number of English letters, which are combined together to express a unique kind of information, but in addition to these, mathematical knowledge is also required, although there is little mathematical knowledge used in some more basic or simple programs, but as the program is more complex, the more mathematical knowledge will be used, such as logical thinking, data structures, algorithms, and so on.

The second point is to learn programming languages.

Artificial intelligence programming languages have a common feature, that is, these languages are designed independently for the problem to be solved, combined with knowledge representation, and completely detached from the Neumann structural characteristics of contemporary computers. They are in turn at a higher level of abstraction than process-oriented high-level programming languages. As a result, programs written in these languages are often inefficient in modern computer environments, either for interpretation or compilation and execution. Especially when the program is large and complex, a lot of system resources (mainly the time and storage space occupied by the processor) will be wasted, and the system performance will be degraded to an intolerable level.

The third point is actual combat.

Theoretical knowledge is only theoretical knowledge and practical application are two different things, no matter how good the theory is, it is useless if it cannot be realized in reality, so after the basic knowledge is learned, you need to carry out practice, and slowly absorb the knowledge learned in the actual case, you will get a different understanding.

At present, the learning content of artificial intelligence majors includes: machine learning, introduction to artificial intelligence (search method, etc.), image recognition, biological evolution theory, natural language processing, semantic web, game theory, etc. The main prerequisite courses required are signal processing, linear algebra, calculus, and programming (preferably with a foundation in data structures).

Judging from the content of the above professional courses, there are still a lot of knowledge related to artificial intelligence that need to be mastered, but the pre-courses have been studied during the undergraduate period, such as signal processing, linear algebra, calculus, etc., if you are in school, this part of the content is well learned, then congratulations, your foundation is good. You can concentrate on learning machine learning and deep learning later.

From a professional point of view, machine learning, image recognition, and natural language processing, any of these are a big direction, and as long as you are proficient in one of them, you are already very powerful. So don't look at a lot of content, some you just need to master, and ultimately what you need to choose is a direction to delve into. In fact, strictly speaking, artificial intelligence is not difficult to learn, but it is not easy to learn, it needs to have a certain foundation related to mathematics, and there is also a period of accumulation.

Artificial intelligence (artificialintelligence) abbreviated as AI, it is a new technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending and expanding human intelligence. More.

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