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So, we now built a whole new kind of restaurant experience, or
restaurant review experience, using classifiers.
Let's dig in and really understand a little bit more what a classifier is,
and some other applications of classification.
So a classifier takes some input x, for example a sentence from the review or
other inputs as we'll see.
It pushes it through what's called a model to output some value y,
that we're trying to predict.
And here it's a class, for example, positive or negative.
So positive in the case of sentiment analysis corresponds with thumbs up
reviews, while negative corresponds with thumbs down.
But this is just one example of classification.
You can look at text, for example, a web page, I want
to figure out which webpages interest me, and I can align them to categories.
For example, is this about education, a page about education,
is it a page about finance, is it a page about technology and so on?
So, there's not just two categories.
There can be three, four, or
even thousands of categories I'm predicting from.
Now, another example of classification,
which really has impacted all of our lives, is in spam filtering.
Some of you might remember, perhaps in the early 2000s, what spam filters were like.
The quality was not very good.
Really, they were all hand-tuned things where somebody said,
oh it has certain words, it must be spam.
But the spammers kept changing the words a little bit,
adding numbers instead of letters, and beating the spam filters.
And what really changed the world of spam filtering.
It changed it so much that I don't even look at my spam folder, actually, sorry if
your message went to my spam folder, I just don't open it, is machine learning.
It's classifiers.
They took the input x of the email and
they fed it through a classifier that predicted whether it's spam or not.
It did that really well.
And it does it by not just looking at the text of the email, but
it looks at other characteristics.
So for example, who sent it.
If it somebody's whose a close friend, or somebody you have lots of vacation,
with it's less likely to be spam.
The IP address is a person sending from the usual computer and so on.
Lots of information.
So this is another really interesting practical application.
In computer vision, we do a lot of classification.
We'll take an image, and figure out what is in that image.
For example, here, the input x are the pixels of the image.
We feed it to a classifier, and
we're going to predict things like is this a dog?
In fact it's a Labrador Retriever, Golden Retriever, or a different kind of dog.
This is actually my dog, Labrador Retriever.
And as we will see later on in the deep learning module,
there's really interesting new ways of doing this with very high accuracy.
Now you can also use classification in medical diagnosis systems, and
in fact this is what your doctor does.
They take your temperature, maybe they look at your x-ray, they look at some
medical tests and they try to make a prediction about what's ailing somebody.
Maybe they say nah, nah, you're just healthy, or you have a cold,
you have the flu, maybe even you have pneumonia.
That's a variable y that's being predicted,
what is the disease that's going on.
Now these days, there's really interesting new things around personalized medicine,
because the prediction doesn't just depend on the standard measurements, but
can be really personalized for me.
Can depend on my particular DNA sequencing, which is pretty exciting and
also my lifestyle, which maybe look something like
or maybe more realistic, something like this.
And so, given all these measurements,
can make an even better prediction at what's ailing me.
Now this idea of classification in off-machine learning,
has really gone much further, even to be able to read your mind.
So when I was at Carnegie-Mellon, Tom Mitchell, one of my friends and colleagues
at his office next door, and he come to me and says, we've done this amazing thing.
Which would take an image of your brain using a technology called FMRI,
which is brain scan, and predict when you're reading a word of text,
whether you're reading the word hammer or the word house.
So it's really reading your mind.
But in fact, he went on to do many interesting things.
For example, if you're looking at a picture of a hammer or a house, but
you train the classified on them you reading words hammers and house,
you're still able to read your mind and figure out what picture you're looking at.
So this is yet
the next frontier of classification to understand how the brain works.
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