Lession1 written before machine learning

In terms of learning, machine learning can be divided into two categories:

Supervised learning (supervised learning), in short, "tagged" learning

Unsupervised learning (unsupervised learning), in short, "no Tag" learning

In order to facilitate machine learning in the future, Mr. Wunda (Andrew Ng) specifically proposed some notation (Chinese translation "notation", to make it the best to gradually familiar with these basic words)

Use X (i) to denote "input" variable " feature"

Use Y (i) to denote "output" variable " Target"

(x (i), Y (i)) is called a training example " Training Example"

{(x (i), Y (i)); i=1,2,3,,m} is called a "training set"

Note that the superscript "(i)" have nothing to does with exponatiation but simply a index into the training set.

Note that (i) does not have anything to do with the index, just the label of the training set.

So-called machine learning, whose real purpose is to train a function h so that any x has the corresponding y we expect, as shown in

There are two important concepts, namely, regression problem and classification problem, in order to ensure that the original intention is not distorted, two definitions are given in English format:

When the target variable that we'll trying to predict are continuous, we call the learning problem a regression Problem.

When y can be on only a small number of discrete values,we call it a classification problem.

When we understand the above basic concepts, we formally enter the machine learning course, in order to ensure that whether you are a researcher or engineering and technical personnel, this blog can play a role for you, the Machine learning section of any class will be divided into two parts: (1) Theoretical deduction part (2) based on MATLAB algorithm practice part. and is elaborated separately. If you are interested in scientific research, then please "know the reason why" to read the theoretical deduction part. If you are eager to design, you can choose the "fast-food" reading algorithm practice section.

Start schoolboys below.

Lession1 written before machine learning