The second type of classification problem we'll consider will be multiclass classification problem where we may have k distinct classes.

Those machine learning problems are called regression problems.

And just to remind you the other most common type of supervised

Here is one other funny thing about what would happen if we were to use linear regression for a classification problem.

Consider a supervised learning classification problem where you have a training set like this.

Let me back up a step further, and let's look at supervised learning more generally.

I see no analogy between your problem and mine.

And treat, therefore, the number of items I sell, as a continuous value.

In this and the next few videos, I want to start to talk about classification problems, where the variable y that you want to predict is discreet valued.

You might be more familiar with the y axis, and for the purpose of this type of problem, it doesn't matter.

For now, we're going to start with classification problems with just two classes zero and one.

That means that us terrestrials occupy a minority.

The output layer in my example in the left has four units.

This problem is akin to the one we had last year.

They don't want to let her in because there's an Israeli stamp on the Laisser Passer.

Y may take on say, for value zero, one, two and three.

Before that you'll have to store these problem reports somewhere that you can classify them, mark them, and evaluate them.

Do they have a common cold?

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

So, you can think of k as also denoting the number of units in the output layer.

And we might be given a total of M data points.

In the next video, we'll start to give a more formal definition of what is machine learning.

We've been talking for a while about housing prediction, and suppose you have a housing classification problem rather than a regression problem, like maybe if you have different features of a house, and you want to predict what are the odds that your house will be sold within the next six months, so that will be a classification problem.

Their happiness is your happiness. Their sadness is your sadness.

What kind of problem do you want to report?

Earlier, we talked about emails, spam classification as an example of a classification problem.

This problem is in his field.

Delicious is a social bookmarking service.

I've been asked to take the long view, and I'm going to tell you what

And two may mean a second type of cancer, a three may mean a third type of cancer.

You know what your problem is, my friend?

You did this to yourself. You're on your own.

Everybody in this room is dealing with their own version of the candle problem.

And that's choice B.

So, for each of these problems, should they be treated as a classification problem, or as a regression problem?

The crisis is now inevitable.

We didn't get to this too much in this class, but this question about universality, questions about what can be computed and how fast it can be computed are really fundamental questions about science.

That problem has been shelved for the time being.

Saying that some problem is NP complete tells us an awful lot about that problem.

In this video, I'd like to show you the hypothesis representation, that is, what is the function we're going to use to represent our hypothesis where we have a classification problem.

So it's choice B.

This is probably the most common thing you'll see in a

Not only financial crises, but because of the sanitary pad research,

So what's the problem?

Let's say I had the situation let me give me a couple of problems if I said three over x is equal to, let's just say five.