All language subtitles for 02_supervised-learning-part-1.en

Machine learning is creating

tremendous economic value today.

I think 99 percent of
the economic value

created by machine
learning today

is through one type
of machine learning,

which is called
supervised learning.

Let's take a look
at what that means.

Supervised machine learning or

more commonly,
supervised learning,

refers to algorithms
that learn x to

y or input to output mappings.

The key characteristic of
supervised learning is

that you give

your learning algorithm
examples to learn from.

That includes the right
answers, whereby right answer,

I mean, the correct label
y for a given input x,

and is by seeing
correct pairs of

input x and desired
output label y that

the learning algorithm
eventually learns to

take just the input
alone without

the output label and gives

a reasonably accurate prediction
or guess of the output.

Let's look at some examples.

If the input x is

an email and the output
y is this email,

spam or not spam,

this gives you your spam filter.

Or if the input is
an audio clip and

the algorithm's job is
output the text transcript,

then this is speech recognition.

Or if you want to
input English and have

it output to
corresponding Spanish,

Arabic, Hindi,
Chinese, Japanese,

or something else translation,

then that's machine translation.

Or the most lucrative form
of supervised learning

today is probably used
in online advertising.

Nearly all the large
online ad platforms have

a learning algorithm that
inputs some information about

an ad and some
information about you

and then tries to figure out

if you will click
on that ad or not.

Because by showing
you ads they're

just slightly more
likely to click on,

for these large
online ad platforms,

every click is revenue,

this actually drives a lot of

revenue for these companies.

This is something I once
done a lot of work on,

maybe not the most
inspiring application,

but it certainly has a
significant economic impact

in some countries today.

Or if you want to build
a self-driving car,

the learning algorithm
would take as input

an image and some
information from

other sensors such as a radar or

other things and then try
to output the position of,

say, other cars so that

your self-driving car can

safely drive around
the other cars.

Or take manufacturing.

I've actually done
a lot of work in

this sector at learning AI.

You can have a learning
algorithm takes as

input a picture of a
manufactured product,

say a cell phone that just
rolled off the production line

and have the learning
algorithm output

whether or not
there is a scratch,

dent, or other defect
in the product.

This is called visual
inspection and it's helping

manufacturers reduce or prevent

defects in their products.

In all of these applications,

you will first train your
model with examples of

inputs x and the right answers,

that is the labels y.

After the model has
learned from these input,

output, or x and y pairs,

they can then take a
brand new input x,

something it has
never seen before,

and try to produce the

appropriate
corresponding output y.

Let's dive more deeply
into one specific example.

Say you want to predict

housing prices based on
the size of the house.

You've collected
some data and say

you plot the data and
it looks like this.

Here on the horizontal axis

is the size of the
house in square feet.

Yes, I live in the United States

where we still use square feet.

I know most of the world
uses square meters.

Here on the vertical axis is
the price of the house in,

say, thousands of dollars.

With this data, let's say a
friend wants to know what's

the price for their
750 square foot house.

How can the learning
algorithm help you?

One thing a learning algorithm

might be able to do is say,

for the straight line to

the data and reading
off the straight line,

it looks like your
friend's house could

be sold for maybe about,

I don't know, $150,000.

But fitting a
straight line isn't

the only learning
algorithm you can use.

There are others that could work

better for this application.

For example, routed and
fitting a straight line,

you might decide that it's
better to fit a curve,

a function that's slightly more

complicated or more complex
than a straight line.

If you do that and make
a prediction here,

then it looks like, well,

your friend's house could be
sold for closer to $200,000.

One of the things you
see later in this class

is how you can decide whether
to fit a straight line,

a curve, or

another function that is even
more complex to the data.

Now, it doesn't seem
appropriate to pick

the one that gives your
friend the best price,

but one thing you
see is how to get

an algorithm to systematically

choose the most
appropriate line or

curve or other thing
to fit to this data.

What you've seen
in this slide is

an example of
supervised learning.

Because we gave the
algorithm a dataset in

which the so-called
right answer,

that is the label or

the correct price y is given
for every house on the plot.

The task of the learning
algorithm is to

produce more of
these right answers,

specifically predicting what is

the likely price for

other houses like
your friend's house.

That's why this is
supervised learning.

To define a little
bit more terminology,

this housing price prediction is

the particular type of
supervised learning

called regression.

By regression, I
mean we're trying

to predict a number from

infinitely many possible numbers

such as the house
prices in our example,

which could be 150,000 or

70,000 or 183,000 or any
other number in between.

That's supervised
learning, learning input,

output, or x to y mappings.

You saw in this
video an example of

regression where the task
is to predict number.

But there's also a
second major type of

supervised learning problem
called classification.

Let's take a look at what
that means in the next video.