So this seems reasonable so far.

Cats fit on the windowsill

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

Children fit in the snow

I don't fit in anywhere I go ?

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

A city fit for a king

Here is what we see coming.

Let's look at some more examples.

So, you can fit things like, you know, access a

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.

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.

like vacuum tube technologies, and relay based technologies.

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

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.

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

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

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.

But settling isn't always such a bad thing.

Suppose you like to decide what degree of polynomial to fit to a data set, sort of what features to include to give you a learning algorithm.

Now let's consider the model selection problem.

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

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

It did not let up.

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

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

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

line of the ellipses like these, but you certainly cannot fit a more complex data set like that shown here.

(Laughter)

Either way they were going to eat it.

Some crazy guy is giving a commencement speech.

One thing a learning algorithm might be able to do is put a straight line through the data or to fit a straight line to the data and, based on that, it looks like maybe the house can be sold for maybe about 150,000.

And then on this side it's going to get really close, but never touch.

Sorry, we can't disclose anything.

So obviously I Googled for it.

I'm different.

Sorry, I didn't hear you.

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

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.

I'm sorry to have kept you waiting so long.

I'm sorry, I have no idea.

I'm very sorry. On the Syrian side, they refuse to accept the fact that you've stamped the Laisser Passer

You know, you have a pizza you've no idea what the price is.

I take no action. I don't get involved.