（原创）Stanford Machine Learning (by Andrew NG) --- (week 1) Linear Regression

Andrew NG的Machine learning课程地址为：https://www.coursera.org/course/ml

在Linear Regression部分出现了一些新的名词，这些名词在后续课程中会频繁出现：

Cost Function	Linear Regression	Gradient Descent	Normal Equation	Feature Scaling	Mean normalization
损失函数	线性回归	梯度下降	正规方程	特征归一化	均值标准化

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Model Representation

• m: number of training examples
• x⁽ⁱ⁾: input (features) of i^th training example
• x_j⁽ⁱ⁾: value of feature j in i^th training example
• y⁽ⁱ⁾: “output” variable / “target” variable of i^th training example
• n: number of features
• θ: parameters
• Hypothesis: h_θ(x) = θ₀ + θ₁x₁ + θ₂x₂ + … +θ_nx_n

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Cost Function

  IDEA: Choose θso that h_θ(x) is close to y for our training examples (x, y).

A.Linear Regression with One Variable Cost Function

Cost Function:    

Goal:    

Contour Plot:

B.Linear Regression with Multiple Variable Cost Function

Cost Function:  

Goal:   

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Gradient Descent 

Outline

Gradient Descent Algorithm

迭代过程收敛图可能如下：

(此为等高线图，中间为最小值点，图中蓝色弧线为可能的收敛路径。)

Learning Rate α:

1) If α is too small, gradient descent can be slow to converge;

2) If α is too large, gradient descent may not decrease on every iteration or may not converge;

3) For sufficiently small α , J(θ) should decrease on every iteration;

Choose Learning Rate α: Debug, 0.001, 0.003, 0.006, 0.01, 0.03, 0.06, 0.1, 0.3, 0.6, 1.0;

“Batch” Gradient Descent: Each step of gradient descent uses all the training examples;

“Stochastic” gradient descent: Each step of gradient descent uses only one training examples.

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Normal Equation

IDEA: Method to solve for θ analytically.

 for every j, then   

Restriction: Normal Equation does not work when (X^TX) is non-invertible.

PS: 当矩阵为满秩矩阵时，该矩阵可逆。列向量（feature）线性无关且行向量（样本）线性无关的个数大于列向量的个数（特征个数n）.

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Gradient Descent Algorithm VS. Normal Equation 

Gradient Descent:

• Need to choose α;
• Needs many iterations;
• Works well even when n is large; (n > 1000 is appropriate)

Normal Equation:

• No need to choose α;
• Don’t need to iterate;
• Need to compute (X^TX)^-1 ;
• Slow if n is very large. (n < 1000 is OK)

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Feature Scaling

IDEA: Make sure features are on a similar scale.

好处: 减少迭代次数，有利于快速收敛

Example: If we need to get every feature into approximately a -1 ≤ x_i ≤ 1 range, feature values located in [-3, 3] or [-1/3, 1/3] fields are acceptable.

Mean normalization:  

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HOMEWORK

好了，既然看完了视频课程，就来做一下作业吧，下面是Linear Regression部分作业的核心代码：

1.computeCost.m/computeCostMulti.m

J=/(*m)*sum((theta'*X'-y').^2);

2.gradientDescent.m/gradientDescentMulti.m

h=X*theta-y;
v=X'*h;
v=v*alpha/m;
theta1=theta;
theta=theta-v;