http://blog.csdn.net/pipisorry/article/details/43529845
机器学习Machine Learning - Andrew NG
courses学习笔记
multivariate linear regression多变量线性规划
（linear regression works with multiple variables or with multiple features）
{x上标i代表第i个trainning example; x下标i代表特定trainning example中的第i个数值}
the hypothesis for linear regression with multiple features(variables)多变量线性回归的假设函数的表示
additional zero feature x0(为了方便表示)
for every example i have a feature vector X superscript I and X superscript I subscript 0 is going to be equal to 1.
Gradient Descent for Multiple Variables多变量的梯度下降
模型表示
通过gradient descent algorithm求解cost func最小值来求parameters θ
{其中左边是单变量线性规划求解参数的gradient descent algorithm;
右边是多变量线性规划求解参数的算法}
Gradient Descent in Practice I - Feature Scaling梯度下降实践1 - 特征缩放
Gradient Descent in Practice II - Learning Rate梯度下降实践2 - 学习率
Features and Polynomial Regression特征和多项式回归
Normal Equation普通方程
from:http://blog.csdn.net/pipisorry/article/details/43529845
Machine Learning - IV. Linear Regression with Multiple Variables (Week 2)
原文：http://blog.csdn.net/pipisorry/article/details/43529845
