What is Machine Learning? (7 min)

What is machine learning? In this video we
will try to define what it is and also try

to give you a sense of when you want to
use machine learning. Even among machine

learning practitioners there isn't a well
accepted definition of what is and what

isn't machine learning. But let me show
you a couple of examples of the ways that

people have tried to define it. Here's the
definition of what is machine learning

does to Arthur Samuel. He defined machine
learning as the field of study that gives

computers the ability to learn without being
explicitly programmed. Samuel's claim to

fame was that back in the 1950's, he wrote
a checkers playing program. And the

amazing thing about this checkers playing
program, was that Arthur Samuel himself,

wasn't a very good checkers player. But
what he did was, he had to program for it to play

10's of 1000's of games against itself.
And by watching what sorts of board

positions tended to lead to wins, and what
sort of board positions tended to lead to

losses. The checkers playing program
learns over time what are good board

positions and what are bad board
positions. And eventually learn to play

checkers better than Arthur Samuel himself
was able to. This was a remarkable result.

Although Samuel himself turned out not to be a
very good checkers player. But because the

computer has the patience to play tens
of thousands of games itself. No

human, has the patience to play that many
games. By doing this the computer was able

to get so much checkers-playing experience that it eventually became a

better checkers player than Arthur Samuel
himself. This is somewhat informal

definition, and an older one. Here's a
slightly more recent definition by Tom

Mitchell, who's a friend out of Carnegie
Mellon. So Tom defines machine learning by

saying that, a well posed learning problem
is defined as follows. He says, a computer

program is said to learn from experience
E, with respect to some task T, and some

performance measure P, if its
performance on T as measured by P improves

with experience E. I actually think he came
up with this definition just to make it

rhyme. For the checkers playing
example the experience e, will be the

experience of having the program play 10's
of 1000's of games against itself. The

task t, will be the task of playing
checkers. And the performance measure p,

will be the probability that it
wins the next game of checkers against

some new opponent. Throughout these
videos, besides me trying to teach you

stuff, I will occasionally ask you a
question to make sure you understand the

content. Here's one, on top is a
definition of machine learning by Tom

Mitchell. Let's say your email program
watches which emails you do or do not flag

as spam. So in an email client like this
you might click this spam button to report

some email as spam, but not other emails
and. Based on which emails you mark as

spam, so your e-mail program learns better
how to filter spam e-mail. What is the

task T in this setting? In a few seconds,
the video will pause. And when it does so,

you can use your mouse to select one of
these four radio buttons to let, to let me

know which of these four you think is the
right answer to this question. That might

be a performance measure P. And so, our
task performance on the task our system's

performance on the task T, on the
performance measure P will improve after

the experience E. In this class I hope to
teach you about various different types of

learning algorithms. There are several
different types of learning algorithms.

The main two types are what we call
supervised learning and unsupervised

learning. I'll define what these terms
mean more in the next couple videos. But

it turns out that in supervised learning,
the idea is that we're going to teach the

computer how to do something, whereas in
unsupervised learning we're going let

it learn by itself. Don't worry if these
two terms don't make sense yet, in the

next two videos I'm going to say exactly
what these two types of learning are. You

will also hear other buzz terms such as
reinforcement learning and recommender

systems. These are other types of machine
learning algorithms that we'll talk about

later but the two most used types of
learning algorithms are probably

supervised learning and unsupervised
learning and I'll define them in the next

two videos and we'll spend most of this
class talking about these two types of

learning algorithms. It turns out one of
the other things we'll spend a lot of time

on in this class is practical advice for
applying learning algorithms. This is

something that I feel pretty strongly
about, and it's actually something that I

don't know of any other university
teaches. Teaching about learning

algorithms is like giving you a set of
tools, and equally important or more

important to giving you the tools is to
teach you how to apply these tools. I like

to make an analogy to learning to become a
carpenter. Imagine that someone is

teaching you how to be a carpenter and
they say here's a hammer, here's a

screwdriver, here's a saw, good luck.
Well, that's no good, right? You, you, you

have all these tools, but the more
important thing, is to learn how to use

these tools properly. There's a huge
difference between, between people that

know how to use these machines learning
algorithms, versus people who don't know

how to use these tools well. Here in
Silicon Valley where I live, when I go

visit different companies even at the
top Silicon Valley companies very often I see

people are trying to apply machine
learning algorithms to some problem and

sometimes they have been going at it for
six months. But sometimes when I look at

what they're doing I, I, I say, you know,
I could have told them like, gee, I could

have told you six months ago that you
should be taking a learning algorithm and

applying it in like the slightly modified
way and your chance of success would have

been much higher. So what we're going to
do in this class is actually spend a lot

of time talking about how, if you actually
tried to develop a machine learning

system, how to make those best practices
type decisions about the way in which you

build your system so that when you're
applying learning algorithm you're less

likely to end up one of those people who
end up pursuing some path for six months

that, you know, someone else could have
figured out it just wasn't gonna work at

all and it's just a waste of time for six
months. So I'm actually going to spend a

lot of the time teaching you those sorts
of best practices in machine learning and

AI and how to get this stuff to work and
how we do it, how the best people do it in

Silicon Valley and around the world. I
hope to make you one of the best people in

knowing how to design and build serious
machine learning and AI systems. So,

that's machine learning and these are the
main topics I hope to teach. In the next

video, I'm going to define what is
supervised learning and after that, what

is unsupervised learning. And also, start
to talk about when you would use each of them.