linear regression.

So, that's linear regression.

linear regression for classification problems.

So, here's our linear regression algorithm.

linear regression isn't a good idea .

So linear regression works well if the data is approximately linear, but there are many examples when linear regression performs poorly.

So that's linear regression with gradient descent.

And thus, using linear regression this way.

Linear regression? Do we compute confidence intervals?

So outliers are particularly bad for linear regression.

Our first learning algorithm will be linear regression.

You now know about linear regression and gradient descent.

And let's say you've implement and regularize linear regression.

This model is called linear regression or this, for example, is actually linear regression with one variable, with the variable being x.

So again, here's the simple regression equation.

When you, do the simple regression equations.

For now, simple regression, it's real easy.

So, here's a simple regression equation, or, a simple path model.

Thrun In this question I quiz you about linear regression.

Right? This was our objective function for the linear regression.

linear regression for fitting a straight line to our data.

This line over here is linear regression to the data.

line intercept plus slope, and that's simple regression.

So we have our simple regression equation. Here.

And we know from just simple regression, a few

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

So remember in simple regression, the standardized regression coefficient was the same as the correlation coefficient.

So, in the last lecture we talked about simple regression.

Then linear regression tries to fit a line that best describes the data.

Are there closed form solutions of the type we found for linear regression?

So this is going to be my overall objective function for linear regression.

Up to this point, I've just been showing you linear relationships, this regression

In the last lecture, we did simple regression, which is just one predictor in the regression equations.

The minimizing of loss is used for linear regression of a new regression problem, and you can write it as follows

That's our gradient descent algorithm, which should be familiar, and you see the linear linear regression model with our linear hypothesis and our squared error cost function.

Before, when we just did simple regression, that was our equation.

In the first two simple regression models, here are the results.

To define linear regression, we need to understand what we are trying to minimize.

And this seems a pretty bad thing for linear regression to have done, right?

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

And I'll quickly review the linear algebra you need in order to implement and use the more powerful versions of linear regression.

Here's a usual hypothesis of linear regression and if you want to compute H of

So, applying linear regression to a classification problem usually isn't, often isn't a great idea.

So again, minimize sums of squares residuals, just like in simple regression.

So first what we're gonna do is just. Run a simple regression.