- [Instructor] So I have a two by two matrix here
and we could view it as having two column vectors.
So the first column can define this vector three, one,
which I've depicted in blue here.
And then that second column, you can view it
as telling us that we have another vector, one, two,
which I have depicted in this pink color.
Now, an interesting interpretation of the determinant
of this two by two matrix is that
the absolute value of the determinant
is the area of the parallelogram defined
by these two vectors.
What I mean by the parallelogram defined
by these two vectors?
Well, imagine taking this bottom vector and shifting it
so it's tail is at the head of this pink vector.
So it would look like this, hand draw it.
Looks something like that.
And then if you were to take this pink vector
and copy it and shift it up into the right.
So its tail is at the head of the original blue vector.
It's going to look something like that.
And so you can see, you can use that technique
to take any two vectors in the coordinate plane.
And they will define a parallelogram.
And it turns out that the area
of this parallelogram is going to be equal
to the absolute value of the determinant
of this matrix here.
So what's that going to be?
Well, we know of figure out the determinant.
It is three times two, which is six.
Minus one times one, which is one, which is equal to five.
And of course the absolute value of five is five.
Now that's pretty cool in and of itself.
We figured out one interpretation of a determinant
which will be useful as we build up
our understanding of matrices.
But another interpretation is to say, all right,
what if A is a transformation matrix,
and I'm just rewriting it.
So we know what a transformation matrix does.
It tells us what to do with the unit vectors, so to speak.
So for example, I have this vector right over here
which is the vector one, zero.
We know that a transformation matrix says, all right
take that one, zero vector
and turn it into the three, one vector.
So turn that into that right over there.
And we know we have the other, or another unit vector.
Let's call this, this right over here
is the zero, one vector.
Goes zero in the X direction, one in the Y direction.
And the transformation matrix says,
hey, turn that into the one, two vector.
But then you can think about it,
it's also not just transforming the individual vectors.
It's also scaling up the area defined by the vectors.
So the area defined by these two original,
we could say unit vectors, we can see it's one by one.
It's that area right over there.
So this transformation is taking us
from an area of one to an area, five.
It's scaling it up by a factor of five.
Now that's reasonably interesting
just for this one unit square.
But because of that it'll actually scale up
the area of any figure.
Let's say had a figure like this.
So this type of oval circle thing, it has some area.
If you apply this transformation matrix
it will look something like this.
I'm just approximating it.
It would look something like that.
So this will tell us that this bigger blob
has five times the area of this original blue blob
because the bigger blob is the image.
Once we've transformed the smaller blob
by this transformation matrix.
So if I were to tell you
that the area of this smaller circle is let's say 0.6
but then we were to apply the transformation.
And someone were to ask you,
what is the area of this bigger blob?
Which is the image of the smaller circle
after the transformation?
Well, you take 0.6, multiply it by the absolute value
of the determinant of the transformation matrix.
You'd multiply it by five.
So 0.6 times five would be three square units.
And a hint at the reason why this works is,
is that any region on the coordinate plane
can be represented as a series of squares.
And then if you apply the transformation
you're really just transforming each
of those individual squares.
So the scaling up of the area would be the same scale you do
to any one of those smaller squares.