In the context of machine learning, what is the relationship between "Normalization" and "norm" "Normalization" and "norm" are used a lot in machine learning

In statistics and applications of statistics, normalization can have a range of meanings.


  In the simplest cases, normalization of ratings means adjusting values
  measured on different scales to a notionally common scale, often prior
  to averaging. In more complicated cases, normalization may refer to
  more sophisticated adjustments where the intention is to bring the
  entire probability distributions of adjusted values into alignment. In
  the case of normalization of scores in educational assessment, there
  may be an intention to align distributions to a normal distribution. A
  different approach to normalization of probability distributions is
  quantile normalization, where the quantiles of the different measures
  are brought into alignment.


In linear algebra, functional analysis, and related areas of mathematics, a norm 


  is a function that assigns a strictly positive length or size to each
  vector in a vector space—except for the zero vector, which is assigned
  a length of zero. A seminorm, on the other hand, is allowed to assign
  zero length to some non-zero vectors (in addition to the zero vector).
  A norm must also satisfy certain properties pertaining to scalability
  and additivity which are given in the formal definition below.


in the context of machine learning, what is the relationship between "Normalization" and "norm"
Let's take L2 norm as an example.

This figure from wiki illustrates "in a right-angled triangle the square of the hypotenuse is equal [to the sum of] the squares of the two other sides"



given a = 3, b = 4, then c = 5.

consider c as a vector,
$
c =
\begin{bmatrix}
3 \\
4 \\
\end{bmatrix}
$

the L2 norm of c is the length (magnitude) of c = 5.

to normalize the vector c, divide each component by its norm 5, then the normalized vector = 
$
\begin{bmatrix}
3/5 \\
4/5 \\
\end{bmatrix}
$

this processing is called normalization.