Or a test result that might have a continuous set of possible values.

We can actually count the values.

Discrete

Discretely of course, ma'm.

But any animal could have a mass anywhere in between here.

Even though this is the way I've defined it now, a finite interval, you can take on any value in between here.

So I might set this be zero or one depending on whether it's been hacked, and have an algorithm try to predict each one of these two discrete values.

Go to Germany, find him. Brother's dead. The family is lost.

learning problem is called the classification problem where we predict discrete valued outputs such as if we are looking at cancer tumors and trying to decide if a tumor is malignant or benign.

Most of the times that you're dealing with, as in the case right here, a discrete random variable let me make it clear this one over here is also a discreet random variable.

And we talked about the classification problem, where the goal is to predict a discrete value output.

The fleet's beginning to break up.

Logistic Regression is actually a classification algorithm that we apply to settings where the

There are discreet values that this random variable can actually take on.

Smoothing was the only planner that actually really worked in a continuous domain.

So technically I guess prices can be rounded off to the nearest cent.

At some level there are a couple differences in methods.

Solomon Golomb.

It looks like she's the anchor for this little domestic disaster.

I've changed the random variable now.

Euler by the way was a famous mathematician, Leonhard Euler.

And we're going to want to square this as well.

While continuous and I guess just another definition for the word discrete in the English language would be polite, or not obnoxious, or kind of subtle.

You hit the ball really hard, it goes flying and it lands with some uncertainty that's Gaussian where the distance in expectation is a 100 m and your variance is 30 m².

It could be 9.58. We can actually list them. So in this case, when we round it to the nearest hundredth, we can actually list of values.

It's a continuous thing, because your steering is continuous, and time is continuous.

We already encountered modes when we looked at bar charts and talked about this a little bit to find the most frequent bar.

And we looked into parametric models like Naive Bayes non parametric models.

This is the first value it can take on, this is the second value that it can take on.

They scale quadratically with the dimensionality of the state space.

Congratulations. You made it through my first motion class.

This point and the regression line, that's the prediction error or residual for that individual.

Say, given 12, find the exponent 3 needs to be raised to.

Compute for me the mean and the variance using the maximum likelihood estimator I just gave you.

These local rollouts are continuous trajectories.

You can see here that a right turn is being executed, followed by a lane shift.

The Dúnedain the men of the West were scattered and few and beset by many dangers

Let's see, our times tables.

The length of the arrow is the absolute value of this.

In the 1 dimensional case, it is mu and sigma squared.

Let me draw in an abstraction of the state space, and discretize the states as shown with these red lines over here.

Let me talk about discrete versus continuous.

As usual, you get a chance to program all these wonderful things.

That is, we have to know the set of available actions to us.

So the absolute value of 3 minus 4i is going to be the distance between 0, between the origin on the complex plane, and that point, and the point 3 minus 4i.