What is machine learning?

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 a definition of what is machine
learning as due to Arthur Samuel. He defined machine learning
as the field of study that gives computers the ability to
learn without being explicitly learned. Samuel's claim to fame was that back in
the 1950, 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 programmed
maybe tens of thousands of games against himself, 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 learned over
time what are good board positions and what are bad board positions. And eventually learn to play checkers
better than the Arthur Samuel himself was able to. This was a remarkable result. Arthur Samuel himself turns out not
to be a very good checkers player. But because a computer has the patience
to play tens of thousands of games against itself, no human has
the patience to play that many games. By doing this, a computer was able to
get so much checkers playing experience that it eventually became a better
checkers player than Arthur himself. This is a somewhat informal definition and
an older one. Here's a slightly more recent
definition by Tom Mitchell who's a friend of Carnegie Melon. 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 out with this
definition just to make it rhyme. For the checkers playing examples,
the experience E would be the experience of having the program
play tens of thousands of games itself. The task T would be the task
of playing checkers, and the performance measure P
will be the probability that wins the next game of checkers
against some new opponent. Throughout these videos,
besides me trying to teach you stuff, I'll 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 mark as spam. So in an email client like this, you might click the Spam button to report
some email as spam but not other emails. And based on which
emails you mark as spam, say your email program learns
better how to filter spam email. 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 me know which of these four you think
is the right answer to this question. So hopefully you got that
this is the right answer, classifying emails is the task T. In fact, this definition defines
a task T performance measure P and some experience E. And so, watching you label
emails as spam or not spam, this would be the experience E and
and the fraction of emails correctly classified,
that might be a performance measure P. And so on the task of systems performance, 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. It turns out that in supervised learning, the idea is we're going to teach
the computer how to do something. Whereas in unsupervised learning,
we're going to 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 might also hear other ghost 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 use 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 what are the other things
to 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 exactly something that I don't
know if any other university teachers. Teaching about learning algorithms
is like giving a set of tools. And equally important or more important than giving you the tools as they
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. 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 people
that know how to use these machine learning algorithms, versus people that
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 trying to apply machine
learning algorithms to some problem and sometimes they have been going at for
six months. But sometimes when I look at what their
doing, I say, 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 will
have been much higher. So what we're going to do in this class is
actually spend a lot of the time talking about how if you're actually trying
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 finally learning
algorithim, you're less likely to end up one of those people who end up persuing
something after six months that someone else could have figured out just
a waste of time for six months. So I'm actually going to spend a lot of
time teaching you those sorts of best practices in machine learning and
AI and how to get the stuff to work and 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 time to talk about when
you would use each of them.