1) Multiple features(多维特征)
2) Gradient descent for multiple variables(梯度下降在多变量线性回归中的应用)
3) Gradient descent in practice I: Feature Scaling(梯度下降实践1：特征归一化)
4) Gradient descent in practice II: Learning rate(梯度下降实践2：步长的选择)
5) Features and polynomial regression(特征及多项式回归)
6) Normal equation(正规方程-区别于迭代方法的直接解法)
7) Normal equation and non-invertibility (optional)(正规方程在矩阵不可逆情况下的解决方法)