Here is what we see coming.

Cats fit on the windowsill

Children fit in the snow

I don't fit in anywhere I go ?

like vacuum tube technologies, and relay based technologies.

A city fit for a king

So Jawbone is a project that you're familiar with, and it has a humanistic technology.

But somehow adding that example out there caused linear regression to change in straight line fit to the data from this magenta line out here to this blue line over here, and caused it to give us a worse hypothesis.

Now let's consider the model selection problem.

lambda is large, then you have a high bias problem and you might not fit your training set so well.

It did not let up.

The image will be scaled to fit the desktop. It will be stretched to fit all four corners.

So this seems reasonable so far.

Either way they were going to eat it.

It seems like a reasonable fit to the data, because, you know, it

Concretely, if you fit some set of parameters theta 0, theta 1, theta 2 and so on to your training set then, the fact that your hypothesis does well in the training set, well, this doesn't mean much in terms of predicting how well your hypothesis will generalize to new examples not seen in the training set.

And in fact, if you compare it to data, it fits very well with the development of cities and economies.

We keep the inner slope, but change the outer slope.

Let's look at some more examples.

So the we have the neutron.

And the hose on that van is just about a perfect match...

And fit in all the right places with lots and lots of star sapphires.

lambda corresponds a high bias where you might not even fit your training set well, whereas a small value of lambda corresponds to, if you can you know freely fit to very high degree polynomials, your data, let's say.

This hypothesis, h(x), with these values of theta zero, theta one, it's really not such a good fit to the data.

Some crazy guy is giving a commencement speech.

In later videos, we will build on this to talk about how to prevent in the problems of overfitting and underfitting as well.

The other thing I like about Coursera's approach is pedagogically, they emphasize mastery of material and again that fits perfectly with how I approach teaching

You are encouraged to experiment with the different styles until you find one which best suits your pattern of work.

X1 through X M, we then fit the parameters, mu 1 through mu n, and sigma squared 1 through sigma squared n, and so these were the formulas similar to the formulas we have in the previous video, that we're going to use the estimate each of these parameters, and just to give some interpretation, mu J, that's my average value of the j feature.

An ill beseeming semblance for a feast.

Nonetheless, if you obtain a surface brightness profile of the many, many ellipticals and then if you fit Sersic law to all of them and plot the value of the

I just can't go on any more.

But settling isn't always such a bad thing.

So there, the model would have been all three equations.

So, if you take this training set and fill a straight

So, for example, we could test the full model, We could test the fit of that relative to a model that takes out the direct effect.

1 WATCH Here is a friar, and slaughter'd Romeo's man,

Here's one more, you know, at this value of theta zero, and at that value of theta one, we end up with this hypothesis, h(x) and again, not a great fit to the data, and is actually further away from the minimum.

One of the things we'll talk about later is how to choose and how to decide do you want to fit a straight line to the data or do you want to fit the quadratic function to the data and there's no fair picking whichever one gives your friend the better house to sell.

In the previous video, we talked about the form of the hypothesis for linear regression with multiple features or with multiple variables.

The supervised learning problem in this case is given a set of labels to fit a hypothesis to it.

Now, so this is how we fit the parameters to this data.

Therefore, when we see that, we see an opportunity to have a business that addresses the need of that segment.

But once we've added that extra example out here, if you now run linear regression, you instead get a straight line fit to the data.

And the Vaucouleurs profile looks like a straight line in those coordinates.