Original subtitles

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.