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This article mainly introduces "how to master mathematics in machine learning". In daily operation, I believe that many people have doubts about how to master mathematics in machine learning. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the doubt of "how to master mathematics in machine learning"! Next, please follow the editor to study!

What is machine learning?

For a given [task T], under the premise of a reasonable [performance measurement scheme P], a computer program can learn the [experience E] of [task T] independently, and with the provision of appropriate, high quality and large amount of experience E, the program's [performance] for [task T] is gradually improved.

The most important thing here is the object of machine learning:

 task Task,T, one or more

 experience Experience,E

 performance Performance,P

That is, with the continuous execution of the task, the accumulation of experience will lead to the improvement of computer performance.

Statement 2:

Machine learning is a branch of artificial intelligence. We use the computer to design a [system] so that it can learn in a certain way according to the provided [training data]; with the increase of the number of training, the system can be continuously learned and improved in [performance]; through the learning model of [parameter optimization], it can be used to predict the output of related problems.

Supervised learning

Given some marks y, to learn x

Unsupervised learning

Learning the new word "military parade" in a statistical way

Reinforcement learning

What can be solved by  machine learning

Prediction problem of given data in 

 data cleaning / feature selection

 determination algorithm model / parameter optimization

 result prediction

 can't solve anything.

 big data Storage / parallel Computing

, make a robot.

Mechine in ML refers to a determined model (classifier or prediction mechanism eg:svm support vector machine)

The parameters of the model are optimized by a given sample.

For example, :

 machine learning: "keep an eye on position 2, she can play fastball easily."

 traditional algorithm: volleyball rules.

For example, the linear example

Ax1 + ax2 + ax3 = y

Data collection-"data cleaning -" [feature engineering]-"[data modeling (high threshold, very important)]

Classify data in different ways

At this point, the study of "how to master mathematics in machine learning" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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