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.

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

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.

We first turn left and down and then right.

Tropic of Cancer

Tropic of Capricorn

KHTML Regression Testing Utility

Here's the cost function that we wrote down in the previous video.

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

Hi, welcome back.

Regression testing output

Regression Testing Status

GUI for the khtml regression tester

Either way it's up to you.

Now let's look at the simple regression with active years in the equation.

The goal is worth 100. A straight motion costs 1.

We'll come back to that again in multiple regression, and in lecture nine in R. And then, the importance of this distinction between unstandardi zed and standardized regression coefficients.

Regression tester for kate

Regression tester for khtml

Another thing I did here is I standardized everything, just to make this a little easier on us.

In this particular example, it

In previous videos, we talked about the gradient descent algorithm and talked about the linear regression model and the squared error cost function.

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

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

We have x of 1, 2, and 3 and y of 4, 7, and 13.

And, in the last segment.

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

But if you turned that money over to the cops, it'll be in the paper. If you didn't...

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

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

Costs

Cost

Cost

Predicting all the prices as functions of one variable X. And another name for this model is univariate linear regression.

Hi. Welcome back to Statistics One.

Clearly a linear regression model is a very poor one to explain this data over here.

Let's look at some examples.

Therefore, y will always be less extreme.

So, this term here.

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

So why do we need yet another learning algorithm?

This time I'm going home.

What brought you back home?

It doesn't matter that much but that's the new cost function we're going to use where y is equal to 1 and you can imagine you should do something pretty similar to logistic regression but it turns out that this will give the support vector machine computational advantage that will give us later on an easier optimization problem, that will be easier for stock trades and so on.