How fitting.

We talked about the fit from data and we even talked about multivariate Gaussians.

The algorithm is amazingly simple.

And on this slide, I'm basically explaining all the problems you have.

I'm going to be fitting very complex high order polynomials that might fit the training set with much more complex functions

For this quiz we will assume a degenerative case of 3 data points and just 1 cluster center.

And they threw up walls political walls, trade walls, transportation walls, communication walls, iron curtains which divided peoples and nations.

Below the threshold we perceive things as they are.

As for the discussion of most of the learning algorithms, we're going to begin by talking about the cost function for fitting the parameters of the network.

Suppose we're fitting a high order polynomial like that shown here, but to prevent overfitting, we're going to use regularization, like that shown here, so we have this regularization term to try to keep the values of the parameters small.

The Berlin Wall?

In that case, given that we're fitting a high order polynomial, this is a usual overfitting setting.

You learned about what a Gaussian is.

In the walls!

That's brick underneath.

Writer's block.

Writer's block?

Stands in the way of what?

Decorative Wall

Wall

Walls on

Working wall bouncing algorithm

Blow the wall.

Just a bit of wall.

And once again here is as if we fit this parameter theta to my cross validation set, which is why I am saving aside a separate test set that I am going to use to get a better estimate of how well my a parameter vector theta will generalize to previously unseen examples.

So, we'll do that for this figure, where maybe d equals 1, were going to be fitting very simple functions where as we are the right of this this may be d equals 4 or relatively may be even larger numbers.

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Yo, Kusakabe!

Buckle up, buckle up!

Get in here.

Suppose we fit a linear regression model with a very high order polynomial, but to prevent overfitting, we are going to use regularization as shown here.

What the artist did was paint an amazing illusion of a landscape, a garden, as though the walls were not walls at all, but views out beyond the fence, beyond the wall.

Look at the picture on the wall.

Rocky walls

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These drawings they belong to Tony Riverside.

I mean, I don't usually scale walls on dates.

They're in the walls.

The first challenge is getting around that wall.

Wall bouncing help

Here, inside these walls

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