机器学习笔记（Washington University）- Regression Specialization-week two

标签：form   int   desc   matrix   min   nali   value   nbsp   with   

1. Modeling seasonality

w1 models the linear trend of the overall process.

w2 models the seasonal component sinusoid with a period of 12

and you do not know when the trend occurs so there exists a phase shift.

2. Regression model

Now we are looking at the coeffiencts,for instanc,e the number of bedrroms, And 

what‘s the predicted change in the value of my house with everything else fixed.

Note:

the interpretation is in the context of what you have in the model. If the

square footage is fixed, the increse of the number of bedroom does not always

increase the value of the house.

3. RSS in matrix form and the gradient

and the gradient of RSS is equal to：

4.The close-form solution

 we can set the gradient to be zero to find the minimum:

and we can get the W that we want.

5. Gradien descent

机器学习笔记（Washington University）- Regression Specialization-week two

标签：form   int   desc   matrix   min   nali   value   nbsp   with   

原文地址：http://www.cnblogs.com/climberclimb/p/6798811.html