Do AI majors need advanced math knowledge?

Many people are no strangers to artificial intelligence, and now there are many artificial intelligence products in our lives. The concept of artificial intelligence was put forward in 1956, after decades of rapid development, artificial intelligence has been slowly popularized, and more and more people have begun to join the artificial intelligence industry, but it is not easy to enter the industry, it is very necessary to learn the relevant knowledge of artificial intelligence. It is very important to have a certain mathematical foundation for learning artificial intelligence, because the basic knowledge of mathematics contains the basic ideas and methods of artificial intelligence problems, and it is also a necessary element for understanding complex algorithms, so what mathematical foundation should we have?

There are many mathematical foundations that artificial intelligence needs to have, mainly including linear algebra, probability theory, formal logic, mathematical statistics, etc., this article will introduce these disciplines and their uses one by one.

1) Linear algebra; Almost all science students and some liberal arts students take this course during their college years, and it is not only the foundation of artificial intelligence, but also the foundation of many other sciences that use modern mathematics as the main method of analysis. The essence of linear algebra is to abstract concrete things into mathematical objects and describe their static or dynamic properties, in the field of artificial intelligence, computers process things in life by abstracting concrete, so linear algebra is very important.

2) probability theory; If linear algebra focuses on abstracting concrete things, then probability theory focuses on the omnipresent possibilities in life. In the field of artificial intelligence, probability theory is no less important than linear algebra by modeling and analyzing the possibilities in life, and then making judgments or operations.

3) formal logic; When the concept of artificial intelligence was first proposed, the founders of this theory believed that the ideal artificial intelligence should be the ability to learn, reason and induct in an abstract sense, which requires a cognitive process, and if we define the cognitive process as the logical operation of symbols, then formal logic is the basis of artificial intelligence, because for artificial intelligence, the essence of cognition is computation.

4) Mathematical Statistics; Although mathematical statistics is based on probability theory, it is fundamentally different from probability theory, which focuses on random variables with unknown distributions, and you can understand that mathematical statistics is an inverse probability theory. For artificial intelligence, the most important thing is to be able to study and analyze random variables with unknown distributions.

The above is the mathematical foundation that the author introduces to you to enter the artificial intelligence industry, which is not complete, because the artificial intelligence industry covers too much content, and the article is just an introduction to some typical content for you, if you are interested in artificial intelligence, you can go deeper.

Don't listen to people's nonsense, if you want to learn the level of most people who are engaged in AI now, elementary mathematics is enough. Although a lot of ** like mathematical formulas flying all over the sky, all kinds of conditional probabilities change around, in fact, the symbols are too messy, many of them are simple derivations, should be written as "obvious", and they are talking about particularly simple and clear things.

As for wanting to break through the existing AI, what is lacking is not mathematics, but understanding and foresight, and it is too late to learn the corresponding mathematics when encountering problems.

First of all, to engage in research and development in the field of artificial intelligence, we must have a solid mathematical foundation, because whether we are engaged in the research and development of machine learning (including deep learning), computer vision, natural language processing or robotics, we all have a common core, and this core is algorithm design, and algorithm design is a mathematical problem in the final analysis.

With the current development of big data and cloud computing, artificial intelligence has a certain guarantee in data and computing power, which also promotes the development of artificial intelligence to a certain extent, and also makes the effect of deep learning have been greatly improved, but compared with data and computing power, the research of algorithms is the core of current research in the field of artificial intelligence. Breakthroughs in algorithms are often difficult, and many core algorithms in the field of artificial intelligence have been used for decades.

Since the current R&D in the field of artificial intelligence is still in the early stage of industry development, there are still a large number of research topics that need to be broken through, so the current demand for talents in the field of artificial intelligence is still dominated by R&D talents, and a solid mathematical foundation is one of the conditions that R&D talents must have. Although a small number of colleges and universities have opened artificial intelligence majors at the undergraduate level, the cultivation of artificial intelligence talents is still dominated by graduate education, and for a long time in the future, if you want to specialize in the research and development of artificial intelligence, graduate school is a more realistic choice.

In the 5G era, the Internet of Things will usher in the industry expectation of comprehensive development, and the Internet of Things, as one of the important application scenarios of artificial intelligence products, will gradually close the combination of the Internet of Things and artificial intelligence in the future, so for learners with a weak mathematical foundation, it is a good choice to start learning from the Internet of Things.

I have been engaged in the Internet industry for many years, and I am currently taking graduate students majoring in computer science, and my main research direction is concentrated in the field of big data and artificial intelligence.

Artificial intelligence does not require much mathematics, and usually uses the basic knowledge of mathematics in college, such as linear algebra, probability theory, statistics, graph theory, etc. Artificial intelligence is mainly to achieve the effect of intelligence by simulating human intelligence, mainly to simulate the information process of human consciousness and thinking, and the basic knowledge of mathematics contains the basic ideas and methods of dealing with intelligent problems, and it is also a necessary element to understand complex algorithms, so to understand artificial intelligence, we must first master the necessary basic knowledge of advanced mathematics. Artificial intelligence is a branch of computer science, and for a machine to learn, it needs an information processing center, which is equivalent to the human brain.

Intellectual behaviors such as learning to think, data processing, judging right and wrong, and logical reasoning will all be carried out here. The processing center is also a place where knowledge is stored, where what has been learned is stored and used when needed. This processing center receives signals from the outside world and outputs the information after the data is processed.

This is essentially a mathematical function. At present, there are six major research fields of artificial intelligence, including natural language processing, computer vision, machine learning, knowledge representation, automatic reasoning and robotics, all of which are inseparable from mathematical knowledge, so if you want to go further in the research and development field of artificial intelligence, a solid mathematical foundation is essential. However, although artificial intelligence will have requirements for mathematical knowledge, it will not be too high, so even some friends who are not very good at mathematical knowledge can also learn artificial intelligence technology, because in learning, you can slowly make up for your mathematical knowledge, and in the early stage of learning artificial intelligence, you will not use particularly complex mathematical problems, mainly some basic knowledge such as linear algebra and probability theory.

If you want to learn artificial intelligence, you also need to see what stage you are in now, if you are still a recent graduate, then the mathematical knowledge has just been learned, and you can naturally cope with the mathematical knowledge used in artificial intelligence, you only need to learn programming well. If you are a friend who has graduated and started working, and it is a related industry, you may have been very proficient in programming, so the main lack is mostly mathematical knowledge, and you only need to review the mathematical knowledge once.

Not too high. Artificial intelligence does not have high requirements for mathematics, and usually uses the basic knowledge of mathematics in universities, such as linear algebra, probability theory, statistics, graph theory, etc.

Hello, I think you can learn as long as you are interested in artificial intelligence. But artificial intelligence needs mathematical calculations, so there is a mathematical base game search to learn faster and better, it is recommended that you learn mathematics simply, and the divine calendar is also helpful for the learning of artificial intelligence.

I. Introduction. Nowadays, artificial intelligence has become one of the hottest topics. More and more people are starting to want to learn about artificial intelligence; So for students who are not very good at mathematics, how to get started with artificial intelligence?

This article shares how to get started with a poor mathematical foundation to learn artificial intelligence, hoping to help you who are about to or have been on the road to artificial intelligence, and avoid some detours.

2. How to learn artificial intelligence.

Artificial intelligence is very broad and contains a lot of directions; Before learning artificial intelligence, you should understand what directions artificial intelligence has and what it can do, and then choose a direction that suits you to learn, which will get a lot more results with half the effort.

Here's a brief introduction to the direction of artificial intelligence.

1.What data types can be processed by AI.

Numeric data.

Numerical data refers to the data that needs to be processed when you are doing an artificial intelligence project, which is numerical or easily converted into numeric types (such as discrete variables such as gender field, city field, and education field), which we usually call numerical data; Common numerical data include financial transaction data, medical data, and lending data.

Text-based data.

data, etc. ** type data.

Type data refers to extracting the meaning of the license plate in **, such as identifying the license plate number in **.

** Identify animals such as cats and dogs, etc.

Audio-based data.

Audio data refers to the identification of content in audio.

Tip: If you want to learn about artificial intelligence, it's recommended that you start with numeric data, as it's relatively easiest to process.

2.What are the technical directions in the field of artificial intelligence?

The field of artificial intelligence is also divided into many technical directions, and the following is a summary of the common technical directions in the field of artificial intelligence.

Machine learning. Machine learning (ML) is a multidisciplinary discipline that involves probability theory.

Statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in how computers simulate or implement human learning behaviors in order to acquire new knowledge or skills, and to reorganize existing knowledge structures to continuously improve their own performance.

It is the core of artificial intelligence, and it is the fundamental way to make computers intelligent, and its application covers all fields of artificial intelligence.

Neural networks. Neural networks are also known as artificial neural networks.

It is a type of machine learning, which is a mathematical model that uses structures similar to the synaptic connections of the brain for information processing. In engineering and academia, it is also often referred to simply as "neural network" or quasi-neural network.

The computational model of the artificial neural network (artificial neural network) is inspired by the central nervous system of animals.

especially the brain), and is used to estimate or can rely on a large number of inputs and one.

I can learn artificial intelligence majors, but it is not recommended to choose socks. In essence, the major of artificial intelligence is the first choice for science and engineering students, as a liberal arts student, or a student who is not so good at mathematics, the future research direction of learning hidden lead artificial intelligence will be much limited, because most of the research directions of artificial intelligence are very strongly related to mathematics, and only a small number of jobs have a slightly lower correlation with mathematics. For example, to do data visualization, data annotation, or algorithm trainers, these positions do not emphasize the principles of mathematics so deeply, and only care about the process or the parameters of the algorithm.

Therefore, people who are not so good at mathematics can also work in the field of artificial intelligence, but their future development prospects will still be limited by many restrictions.

With the increasing demand for talents in the field of artificial intelligence, the traditional graduate education in the scale of talent training has been far from meeting the market requirements, so many key universities have taken the lead in setting up artificial intelligence majors at the undergraduate level, so if you want to learn the direction of artificial intelligence, then it is an ideal choice to choose artificial intelligence during the undergraduate period.

Artificial intelligence is a typical interdisciplinary discipline, not only the amount of knowledge is relatively large, but also the learning difficulty is relatively high, so the choice of artificial intelligence major should have a strong learning ability, and at the same time, the foundation of mathematics and physics should be relatively solid, especially the foundation of mathematics, which is very important for the subsequent learning process.

Although it is ideal to choose an artificial intelligence major during the undergraduate period, because there are not many colleges and universities that offer artificial intelligence majors, and many colleges and universities have just established artificial intelligence majors, the space for choice will be relatively small. In fact, in addition to artificial intelligence majors, computer science and technology majors, software engineering majors, Internet of Things engineering majors and big data majors can also be considered at present, among which computer science and technology majors and big data majors can be considered emphatically.

Computer science and technology majors have a wide range of knowledge, and many students in this major will choose artificial intelligence when they are in graduate school. In addition, the computer science and technology major will also set up some artificial intelligence-related major directions during the undergraduate period, which will also lay the foundation for subsequent study. If you have a plan to go to graduate school in the future and still want to focus on artificial intelligence-related directions, then you can focus on computer science and technology, which has a relatively large choice space.

Finally, although the big data major has been established for a relatively short time, the big data technology system is relatively mature, and the industry cases of big data are becoming more and more abundant, so the choice of big data major will also have a better learning experience.