Which of these would be a good algorithm or technique for doing this classification into things like people, places, and drugs.

And that's called the support vector machine, and compared to both the logistic regression and neural networks, the

I just drew in magenta. The curve that I just drew purple and magenta.

Armed with these definitions, we are now ready to build the support vector machine.

In the next video we'll start working out the details of the logistic regression algorithm.

Except that we don't sum over the terms corresponding to these bias values

And it will compute the test error as follows.

How about if we were doing a classification problem and say using logistic regression instead.

And the term logistic function, that's what give rise to the name logistic progression.

And so this builds in an extra safety factor or safety margin factor into the support vector machine.

My cost function, j of theta is now going to be the following is minus one over m of a sum of a similar term to what we have in logistic regression.

So here's a quick quiz for you.

The cost function we use for the neural network is going to be a generalization of the one that we use for logistic regression.

Hi, welcome back.

For logistic regression, we used to minimize the cost function j of theta that was minus 1 over m of this cost function and then plus this extra regularization term here, where this was a sum from j equals 1 through n, because we did not regularize the bias term theta zero.

logistic regression and we're seeing what the mathematical formula is defining the hypothesis for logistic regression.

And, in the last segment.

First, we're going to get rid of the 1 over m terms and this just, this just happens to be a slightly different convention that people use for support vector machines compared to for logistic regression.

And today we're going to do multiple regression analysis in R.

All, simultaneously in one analysis.

So this is lecture nine.

We already have linear regression and we have logistic regression, so why do we need, you know, neural networks?

If you want to apply logistic regression to this problem, one thing you could do is apply

Let F of XP, or linear function, and the output of logistic regression is obtained by the following function

That's the term that a single training example contributes to the overall objective function for logistic regression.

Consider a supervised learning classification problem where you have a training set like this.

Okay. That wraps up this segment.

But for the sake of time, I didn't wanna go through that here.

You can do this by hand if you had to, if you lost all electricity and there, there's no internet and no R software.

In the last few lectures we have covered in detail multiple regression analysis.

less than 0 because that corresponds to hypothesis of outputting a value close to 0.

Machine and where you minimize that function then what you have is the parameters

But that's just the name it was given for historical reasons so don't be confused by that.

Hi, welcome back to Statistics one.

Once you have those basic skills down.

So the main topic of this segment is just again estimation of regression coefficients in multiple regression.

And the reason we did that is so that we could see exactly how the regression coefficients are estimated in a multiple regression.

I've organized this lecture into two segments.

Hi, welcome back. We're in Lecture ten, Segment two.

The second bit of notational change, we're just designating the most standard convention, when using as the SVM, instead of logistic regression as a following.

We're up to lecture eight, almost have way through the course.

Then, we can apply that to the multiple regression example.

Hi, welcome back to Statistics one. We're up to lecture nine.

And, just to give this another name, this is also called multivariate linear regression.

The important concepts to take away from this segment are, again, understanding the equation and the components of the equation, how to interpret the, both the unstandardized and the standarized regression coefficients.