in this video we will start
to talk about a new version
of linear regression that's more powerful.
One that works with multiple variables
or with multiple features.
Here's what I mean.
In the original version of
linear regression that we developed,
we have a single feature x,
the size of the house, and
we wanted to use that to
predict why the price of
the house and this was
our form of our hypothesis.
But now imagine, what if
we had not only the size
of the house as a feature
or as a variable of which
to try to predict the price,
but that we also knew the
number of bedrooms, the number
of house and the age of the home and years.
It seems like this would give
us a lot more information with which to predict the price.
To introduce a little bit
of notation, we sort of
started to talk about this earlier,
I'm going to use the variables
X subscript 1 X subscript
2 and so on to
denote my, in this
case, four features and I'm
going to continue to use
Y to denote the variable,
the output variable price that we're trying to predict.
Let's introduce a little bit more notation.
Now that we have four features
I'm going to use lowercase "n"
to denote the number of features.
So in this example we have
n4 because we have, you
know, one, two, three, four features.
And "n" is different from
our earlier notation where we
were using "n" to denote the number of examples.
So if you have
47 rows  "M" is the
number of rows on this table or the number of training examples.
So I'm also
going to use X superscript
"I" to denote the
input features of the "I" training example.
As a concrete example let say
X2 is going to
be a vector of
the features for my second training example.
And so X2 here is
going to be a vector 1416,
3, 2, 40 since those
are my four
features that I have
to try to predict the price of the second house.
So, in this notation, the
superscript 2 here.
That's an index into my training set.
This is not X to the power of 2.
Instead, this is, you know,
an index that says look at the second row of this table.
This refers to my second training example.
With this notation X2 is
a four dimensional vector.
In fact, more generally, this is
an in-dimensional feature back there.
With this notation, X2 is
now a vector and so,
I'm going to use also Xi
subscript J to denote
the value of the J,
of feature number J
and the training example.
So concretely X2 subscript 3,
will refer to feature
number three in the
x factor which is equal to 2,right?
That was a 3 over there, just fix my handwriting.
So x2 subscript 3 is going to be equal to 2.
Now that we have multiple features,
let's talk about what the
form of our hypothesis should be.
Previously this was the
form of our hypothesis, where x
was our single feature, but
now that we have multiple features,
we aren't going to use the simple representation any more.
Instead, a form
of the hypothesis in linear regression
is going to be this, can be
theta 0 plus theta
1 x1 plus theta 2
x2 plus theta 3 x3
plus theta 4 X4.
And if we have N features then
rather than summing up over
our four features, we would have
a sum over our N features.
Concretely for a particular
setting of our parameters we
may have H of
X 80 + 0.1 X1 +  0.01x2 + 3x3 - 2x4.
This would be one
example of a hypothesis
and you remember a
hypothesis is trying to predict
the price of the house in
thousands of dollars, just saying
that, you know, the base
price of a house
is maybe 80,000 plus another open
1, so that's an extra,
what, hundred dollars per square feet,
yeah, plus the price goes up
a little bit for each
additional floor that the house has.
X two is the number of
floors, and it goes
up further for each additional
bedroom the house has, because
X three was the number
of bedrooms, and the price
goes down a little bit
with each additional age of the house.
With each additional year of the age of the house.
Here's the form of a hypothesis rewritten on the slide.
And what I'm gonna do is
introduce a little bit of
notation to simplify this equation.
For convenience of notation, let
me define x subscript 0 to be equals one.
Concretely, this means that for
every example i I
have a feature vector X superscript
I and X superscript
I subscript 0 is going to be equal to 1.
You can think of this as defining
an additional zero feature.
So whereas previously I had
n features because x1, x2
through xn, I'm now defining
an additional sort of zero
feature vector that always takes
on the value of one.
So now my feature vector
X becomes this N+1 dimensional
vector that is zero index.
So this is now a n+1
dimensional feature vector, but
I'm gonna index it from
0 and I'm also going
to think of my
parameters as a vector.
So, our parameters here, right
that would be our theta zero,
theta one, theta two, and so
on all the way up to theta n,
we're going to gather
them up into a parameter
vector written theta 0, theta
1, theta 2, and so
on, down to theta n.
This is another zero index vector.
It's of index signed from zero.
That is another n plus 1 dimensional vector.
So, my hypothesis cannot be
written theta 0x0 plus
theta 1x1+ up to
theta n Xn.
And this equation is
the same as this on
top because, you know,
eight zero is equal to one.
Underneath and I now
take this form of the
hypothesis and write this
as either transpose x,
depending on how familiar
you are with inner products of
vectors if you
write what theta transfers x
is what theta transfer and
this is theta zero,
theta one, up to theta
N. So this
thing here is theta transpose
and this is actually a N
plus one by one matrix.
It's also called a row vector
and you take that and
multiply it with the
vector X which is X
zero, X one, and so
on, down to X n.
And so, the inner product
that is theta transpose X
is just equal to this.
This gives us a convenient way
to write the form of the
hypothesis as just the inner
product between our parameter
vector theta and our theta
vector X. And it
is this little bit of notation,
this little excerpt of the
notation convention that let
us write this in this compact form.
So that's the form of a hypthesis when we have multiple features.
And, just to give this another
name, this is also
called multivariate linear regression.
And the term multivariable that's just
maybe a fancy term for saying
we have multiple features, or
multivariables with which to try to predict the value Y.