Quick Overview: Okay so so this is to say that uh y bar the vector y bar is And so I've skipped over pretty much all of this work and the details are listed out in section I've got the the contents of this which is one through 8 uh A B C D and uh 5 4
Stats 102b Lesson 1 3 - Detailed Overview & Context
Okay so so this is to say that uh y bar the vector y bar is And so I've skipped over pretty much all of this work and the details are listed out in section I've got the the contents of this which is one through 8 uh A B C D and uh 5 4 ... W hat these are going to be our predicted values T hat and predicts Okay okay and so you know let me just kind of do a simple numeric example here okay so we could have a the data ... w0 and w1 okay and rather than using the total squared error it uses the mean squared error so we're going to do
Density so if some value y comes from with mean mu Sigma 2 this is Okay so this is our likelihood function based on our data and and if I plot this so all I'm doing is just plotting from 0 to ... simple numeric example I've got the values What do we get here uh is something right times uh yeah I guess ... keeping just one principal component okay so from 634 I'm going to just keep one I'm going to do X1 so this is D B transpose x1 plus B is equal to plus
Seven well actually so if the believes it is a seven the um the answer so with the 10 nodes 0 Ok so we currently have some gradient matric our weight matrix W
