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This course address classification, which is one of the most widely used,
most fundamental areas of machine learning.
If you understand classifiers,
you'll also understand basically the rest of machine learning and the techniques
we use here is what most people in the industry need to be successful.
We discuss how much learning is about input data
which can be pushed through some machinery algorithm
which outputs what we think about this kind of intelligence from the data.
In this course, we're going to build classifiers, so
classifier takes into input But some x or some features of our data.
And as output makes a prediction which a discrete class or
category or label for the data.
And we're going to see a ton of different examples of how this is used for
in practice.
The goal of a classifier is to learn a mapping from the input x to the output y,
those classes.
The example that we discussed in the first course was a sentiment classifier,
where we're given that input sentence x, like easily the best sushi in Seattle.
We fed that through the sentiment classifier,
which then told us an output y.
That was either, yeah that is a positive sentence.
Or nay, that is a negative sentence.
And we can use these sentences, these predictions in a wide range of ways,
as we'll see soon.
A general classifier about is about, taking some input x,
pushing it through some model, which predicts why that might be, for
example, two classes or multiple classes of, say, positive or negative, or
as we will see, it could be three, four or more categories.
Let's suppose for example I have a web page, and
I want to figure out what ads to show on this web page.
So I need to figure out what this web page is about.
The goal here is to take the text of the web page and categorize it automatically
as to whether it's kind of an educational site and educational type ads.
Whether it's kind of a site about finance or an article about finance and
we need that kind of ad.
Or one about technology and so on.
So a classification is not jut binary, positive or negative, but
it can be one of multiple categories, or multiple classes.
Perhaps the most common type of classifier that we see everyday,
every time we open up our email is a famous spam filter.
So the spam filter takes every time an email arrives.
Makes a prediction whether this is a spam email, it should be ignored or not spam.
And that prediction needs to be made based not just on the text of the email but
on other information we get from that email.
Like who that sender was what the IP address of that sent message is.
Other messages that the sender sent, and so on, and, from that information, we're
going to learn the mapping, from those inputs, to whether it's spam, or not.
And, those spam filters gotten so much better over the years.
So, I remember, early on, we just used keyword search,
or keyword classifiers, and they weren't very good.
But, today I don't even check my spam folder anymore.
So if you send me an email and didn't open it, maybe it's in my spam folder.
Sorry.
We can build all sorts of classifiers though.
We can use, for example, image data.
So given this particular input, my dog, the image pixels.
I want to make a prediction from a certain category.
So from this famous image net data set,
where there's a thousand different categories you might want to predict.
So for example,
if you want to know if it's a labrador retriever, a golden retriever, and so on.
What kind of dog it is, and that's the output label y that we might want.
Now the idea of classifiers can be extremely useful for
a wide range of domains.
One that I'm particularly excited about is the area of personalized medicine,
which I think is going to change the world.
So today if I don't feel so well I might put a thermometer under my arm and
check out my temperature, or a doctor might order
an X-Ray to see what's going on in my chest, or maybe use some lab tests.
And based on that information, goes through some crossfire,
which maybe it's in the doctor's head or maybe it's an automated system,
that tries to make a prediction as to what condition I might have.
But what's annoying about how medicine is done today
is that based on the same conditions, we make the same predictions for me or for
you independent of the fact that we're really different people.
Personalized medicine aims to totally change that.
So it's going to look at our DNA sequences because we're genetically different and
find a good treatment for each one of us.
And maybe even look at our lifestyle which might say something about what I'm
prone to.
Maybe that's your lifestyle, maybe my lifestyle is more like this.
And so based on that kind of information, we can predict what condition I have and
what have treatment is going to be the most effective for me.
And that's an example of classification in the real world.
Perhaps one of the most fun and surprising examples of classification
is work that one of my colleagues Tom Mitchell did, which is pretty amazing.
You take a scan of your brain as you look at a word, and
based on that image from what's called an FMRI,
he can make a prediction as to what kind of word you're reading.
So for example, based on the image of your brain, it can predict if you're reading,
say, the word hammer or the word house.
Which is basically reading your mind.
And I've been talking to Tom for a long time about this topic.
More than ten, 15 years.
And over that time, the concept results they have,
had evolved from very basic things.
So amazing things.
So, for example today,
they can train a classifier on your brain images based on words that you read, and
then use it to predict something from my brain images based on pictures that I see.
So picture of a hammer instead of the actual word hammer.
And that is an incredible kind of evolution,
an incredible kind of analysis that you can do from brain data.
Really, really cool example classification.
Read your mind.
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