Now let's consider the model selection problem.

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

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.

Here we see Hubble diagrams with the main trend line subtracted a particular model subtracted and then residuals fit for variety fodder models.

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

Finally, given this hypothesis representation, what we need to do, as before, is fit the parameters theta to our data.

A city fit for a king

Cats fit on the windowsill

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

So this seems reasonable so far.

Children fit in the snow

I don't fit in anywhere I go ?

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.

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

Here is what we see coming.

And this global minimum corresponds to this hypothesis, which gives me a good fit to the data.

Tim Berners Lee Can you say data ?

Let's look at some more examples.

The judge was not impressed to the tune of 10.9 million dollars.

You say, This photograph, it's about it depicts this person.

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.

However, my boss always asked if I had the data to prove it would sell, and to think of product development after analyzing market data.

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zeptopascals

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It did not let up.

like vacuum tube technologies, and relay based technologies.

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You're going to stand in front of a panel of engineers.