So that's separable.

That's what this playlist will deal with, ordinary differential equations.

So there's two big classifications.

Let me give you another word that's really important called standard deviation.

This is the expression for the mean.

We take the product of the variance of x and the variance of y modally with the normalizers and we get something quadratic even in variance space.

Homogeneous differential equation.

So we'll just assume that that is our psi.

They say that y of 0 is equal to negative 1.

Electrolytically produced, yes?

We'll let R do the work.

Or if you wanted to write it out, psi is this thing.

I just took the antiderivative of both sides.

And the answer is 14.14 m, which is the same as 10 m 2.

The kalman filter in contrast, under certain assumptions, is quadratic.

We're going to start embarking on higher order differential equations.

And this one is called homogeneous.

But anyway, there we have it.

We could call that c1 or something.

Dispersion

Variance

But if you look here, you actually could have figured out that this is actually a separable equation.

We're almost at the correlation matrix.

We won't be doing well on the cross validation set.

Just for the exercise of applying the formula, compute for me the mean and the variance.

What does a homogeneous differential equation mean?

And then you could solve for c.

But anyway, that's just a little technicality.

And it might seem really fancy.

So they say y1 of x is equal to e to the minus 3x.

And actually, I do see more of a connection between this type of equation and milk where all the fat is spread out, because if you think about it, the solution for all homogeneous equations, when you kind of solve the equation, they always equal 0.

I would say a lot easier than what we did in the previous first order homogeneous difference equations, or the exact equations.

And let's say we try to do this, and it's not separable, and it's not exact.

I gave you the formulas for how to compute the mean of the outcome.

Now before we go on, in the next one I'll show you some fairly straightforward differential equations to solve.

And what we're dealing with are going to be first order equations.

So it's the square root, again, that's square root of variance, which is sums of squares over n.

Well, you might vaguely remember that the sum of two coin flips or sum of any two of anythings you do, the variances just add up.

Method

A system?

So I'll just re write that as c.

Is it possible to represent this function as a Gaussian?

Isotropic

This is now easily repeated for the variance.

Suppose we give a data set of positive samples and negative samples.