It's worth twice as much.

You can have it the other way around.

And so lets see is we can simplify this a little bit.

This is equal to this.

A book worth reading is worth reading twice.

This book is worth reading twice.

So, the maximum for the first row is 8, max of second row is 7, max of the third row is 9 and so this allows you to take maxes.

And I'd like you to count parameters.

We'll use another example to show how variable elimination works.

The characteristic of classifcation is that the target labels or the target class is discrete.

Now what are the outcomes that satisfy our conditions?

A compound event is just an event that has more than one possible event that satisfies it.

This book is worth reading twice.

And so this second column turns out to be the predictions of the second hypothesis of the second hypothesis up there, and similarly for the third column.

The other one, led by Gerard de Vaucouleurs and his collaborators in

So that magenta is what that first graph is doing.

So for example, x(1) refers to the input value for the first training example so that's 2104.

That's what it means to say that this whole process is biased by n.

Which is the more expensive of the two?

The first die is going to have some number on it.

So we want to roll doubles. How many ways can we roll doubles?

So that's the change in y over, and we take the first x point.

And that would give us the sum of squared residuals.

And I'll show you why that's true in a second.

A bird in the hand is worth two in the bush.

A bird in the hand is better than two in the bush.

So that is going to be one over 2M of, my usual cost function.

So, the maximum of the first column is eight, max of the second column is nine, the max of the third column is seven.

People say, What's the solution? Here it is.

What I want to get into your brains is not the complexity of the formula. I want you to really understand how this formula is constructed.

Disdyakisdodecahedron

Computer script errors by, you know, taking the difference between your predictions and the y values and taking the element of y squaring and then finally computing the cost function J. And Octave knows that J is a value I want to return because J appeared here in the function definition.

Ditrigonal dodecadodecahedron

The probability of getting doubles when you rolled two six sided dice, numbered from 1 to 6.

Change in y, change in y, over change in x, is equal to we take the first y point, which is 3.

I think here it should be clear, that if I were to draw vertical line, let's say right there, for x is equal to 1, it has two definitions.

So, in some sense it... it's more powerful.

The search is not for the solution of the problem, but for problems themselves worth solving.

Gaussian distribution with some parameter Mu and some parameter sigma squared.

The problem with parameter estimation is, given my data set I want to figure out, I want to estimate, what are the values of Mu and sigma squared.

Great dirhombicosidodecahedron

Twice two is equal to four.

Icosidodecahedron

Table 11 4.

So I can look at my first model, my second model, fits with these different values of the regularization parameter and evaluate them on my cross validation set basically measure the average squared error of each of these parameter vectors theta on my cross validation set.