The contents are about notes and equations taken from 《Machine Learning》, which is taught by Andrew Ng, in Coursera.

key words: normal equation

### Normal Equation

gradient decent 提供了一种最小化 loss function 的方法，用的是不断迭代的方式求 $\theta$。

normal equation 则是用类似解方程的办法来求 $\theta$。此外，对于 normal equation 的方法，也不用再去 feature scaling 了。

Need to choose alpha No need to choose alpha
Needs many iterations No need to iterate
$O(kn^2)$ $O(n^3)$, need to calculate inverse of $X^T X$
Works well when n is large Slow if n is very large
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