like vacuum tube technologies, and relay based technologies.

Let's look at some more examples.

Here is what we see coming.

Cats fit on the windowsill

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

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

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

It did not let up.

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

So this seems reasonable so far.

So the we have the neutron.

Children fit in the snow

I don't fit in anywhere I go ?

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

A city fit for a king

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

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

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

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.

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

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.

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.

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

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.

I just can't go on any more.

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.

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

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.

Either way they were going to eat it.

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

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.

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

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.

We've already seen a lot of times the problem of overfitting, in which just because the

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

So my factory can produce a million cupcakes and I sell a million cupcakes.

An ill beseeming semblance for a feast.

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

We must try to get rid of it, the sister now said decisively to the father, for the mother, in her coughing fit, was not listening to anything.

It's crap.

In the rear there was the dim outline of a garden, which had been planted but had never received its first hoeing, owing to those terrible shaking fits, though it was now harvest time.

So we could say a is equal to negative 2 same idea there and then you square it.