Next, given a training set, of M, unlabled examples,

We require only a limited set of training data

Our training data is a set of movie titles

like on a training set as we vary the parameter

And for the case of when we have a large training set, not just a training set of one example, here's what we do.

That's an index into my training set.

More formally, in supervised learning, we have a data set and this data set is called a training set.

So, if you take this training set and fill a straight

You only have a training set and the test set and that's because that's good practice.

And suppose we have a training set like this of this of

Next, we're going to loop through our training set.

That is, you're fitting the training set very well.

We saw that with the training set like our training set of housing prices and we feed that to our learning algorithm.

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

learning algorithm fits a training set well, that doesn't mean there's a good hypothesis.

Suppose we have a training set of m examples like that shown here.

The approach that we're going to take is that given this training set, given the unlabeled training set, we're going to build a model for p of x.

Let's say your training set size is M equals 100,000,000.

label training set, with, let's say, a few label examples of cars and a few

Worth more today to grade the training set and relatively less to the test set.

In linear regression we have a training set like that shown here. Remember our notation M was the number of training examples.

Looking at the training set, it is pretty clear what a good hypothesis is.

It's usually the same thing as the j of theta that we'll be writing so far, this is just a training set error you guys measure on your training set.

Because, M when trading set size, right, have three training examples.

We set up carpet weaving units, vocational training for the women.

Because for the specific training set I have or my 3 training examples are (1, 1), (2, 2), (3,3).

The first portion is going to be our usual training set and the second portion is going to be our test set, and a pretty typical split of this all the data we have into a training set and test set might be around say a 70 , 30 split.

Right now I'm training in preparation for the day we set off.

So what I want really is to sum over my training set.

Maybe even zero, because it will fit the training set really well.

We have supervised learning, in which the training set is a bunch of input output pairs

Whereas in contrast your error on a cross validation set or your cross function like cross validation set, will be much bigger than your training set error.

And the key that distinguishes these two cases is if you have a high bias problem your training set error will also be high as your hypothesis just not fitting the training set well.

And therefore fail to generalize the new examples not in the training set.

Before you know sending the first 70 in the training set and the last 30 of the test set.

Let's start by talking about the case of when we have only one training example, so imagine, if you will that our entire training set comprises only one training example which is a pair xy.

Let's say that we have an unlabeled training set of M examples, and each of these examples is going to be a feature in Rn so your training set could be, feature vectors from the last

Concretely, if you fit some set of parameters theta 0, theta 1, theta 2 and so on to your training set then, the fact that your hypothesis does well in the training set, well, this doesn't mean much in terms of predicting how well your hypothesis will generalize to new examples not seen in the training set.

That should be better to send a random 70 of your data to the training set and a random 30 of your data to the test set.

And just, for this particular training set, we actually end up fitting it perfectly.

And the first piece is going to be called the training set as usual.

The new identity need not be present in our training set or even correspond to a real person.

It's a vocational training path.

Yes. lt's a training ground.

So let's try to choose values for the parameters so that at least in the training set, given the x's in the training set, we make reasonably accurate predictions for the y values.