Hi I'm Artemy Kolchinsky. In these
tutorials we'll talk about what machine
learning is examples of machine learning
problems and some of the reasons why
it's become so prevalent in the news
let's just jump in and talk about what
machine learning is first you might be
curious how it differs from another term
you've heard which is artificial
intelligence I would say that artificial
intelligence is the general science of
creating intelligent automated systems
this could be things like chess playing
software software for controlling robots
or industrial processes and many many
other examples machine learning is a
subset of artificial intelligence and
it's particularly aimed at designing
software which can automatically learn
to perform intelligent things from
usually data that's provided to it and
from this point of view in some ways is
actually very closely related to
statistics because statistics is also
concerned about making inferences from
data and using them to predict other
things now to get a kind of schematic
idea of the difference between these two
we might consider two different
artificial intelligence systems one of
them is what's called the traffic
avoidance the traffic collision
avoidance system T caste and it's
basically a system that was developed
many decades ago to help planes avoid
each other and avoid mid-air collisions
this is essentially long list of if
statements and conditions that says when
to trigger alarms when to tell one plane
to go up and another plane to go down
and so on and these are hard coded rules
that were designed to help avoid
collisions this is not a machine
learning problem because it's
essentially some hard coded program now
I'm an example of what I would say is a
machine learning problem is the
following given some data make a
statistical model of something like
personal annual income depending on
things like age gender years in school
geographical zip code and so on and you
these what would be called independent
variables to make a prediction about
let's say how much money a person makes
in a year now if you've taken a
statistics class you might think that
this is just could be solved with a
simple linear regression and it turns
out that that's true that's one way to
approach the problem and simple linear
regression is a simple case of machine
learning it's using data to build a
model that then can be used to make
predictions about other things if you
want a more cinematic example of the
difference between AI and machine
learning you can think of the computer
in 2001 a Space Odyssey Hal 9000 which
was seemed like he was essentially
programmed to carry out a single-minded
task of the space mission and that would
be a classic AI example compare that to
the computer in the 1980s classic war
games which was trained to learn to play
global thermonuclear war and then could
learn to play things like tic-tac-toe
and that would be an example of a
machine learning a AI okay moving on I'm
gonna talk about two major areas of
artifice of machine learning supervised
learning and unsupervised learning in
supervised learning there's essentially
a right answer to a question and we
train the algorithm using but showing it
what the right answers are examples of
this might be the following I give you
an image and I ask you to classify it as
either cat or dog there's clearly a
right answer for most images or I give
you an audio recording and I ask you to
parse the text that was actually said in
that audio recording or finally I might
give you some kind of stock market
historical data and I ask you to predict
what the stock market will be now the
stock market will reach a certain level
and so we can be right or wrong in this
tutorial we'll mainly talk about
supervised learning however you should
also know about this other area of
machine learning called unsupervised
learning and in unsupervised learning
there's no right answer per se but what
we wish to do is find meaningful
patterns in the data this might be
something like take some very high
demand
dataset and represent it in a low
dimensional space it might be something
like take a dataset and cluster it into
groups so that's another example you see
on the screen where you can we visually
can see that there's a scatter plot and
there's some grouping there what is the
right grouping well there might not be
one right grouping but we still would
like to consider things it also includes
things like outlier detection anomaly
detection where it's not exactly clear
what the right answer is but it's still
useful to have some kind of pattern
finding algorithm in addition to
supervised learning and unsupervised
learning there's many many other
mixtures and variations and alternatives
to these such as transfer learning
semi-supervised learning and so on we
won't have time to get into most of
these but you should know that this the
domain areas of machine learning are
very large but supervised learning is
probably the one that's had the most
successes recently and it's the one
we'll talk most about okay so how does
supervised learning actually work in
supervised learning we're given what's
called a training data set I'm gonna use
the example of taking images and mapping
them to a label dog or cat depending on
what's shown in the image our training
data set would then consist of a whole
bunch of images and for each of those a
label is this a dog or is this a cat now
we take that training data set and we
take what's called a statistical model
or generally some kind of machine
learning model and we take a learning
algorithm and we'll talk about what
happens in this process when they're
combined but when they're combined we
say that we train the model and the
outcome of this is a trained model okay
so this train model has learned
something about the association between
images and the label dog or cat and once
we've trained it we can actually give
this train model an image it has never
seen before and it's going to make a
prediction is this a dog or a cat and if
we've done a good job then it should be
right about its prediction and we'll
talk about that
more later okay so just one thing that's
really important to remember is that
we've talked about some examples like a
classic one learning whether images or
dogs or cats but there's many many many
problems in the world that can be seen
as supervised learning problems for
example playing a video game
automatically learning how to play a
video game is an example of a supervised
learning problem we can think of the
supervised learning problem is learning
and mapping from the current state of
the video game screen to what should my
next move be should it be left right
jump or fire for example and one way
that such models are trained in a term
that you will hear is reinforcement
learning reinforcement learning is a
type of supervised learning in which
essentially the algorithm performs many
run throughs through some kind of
sequence of events and at the end of
these sequence of events it gets a
positive or negative reward and it
actually changes how it runs through the
sequence of events so as to maximize the
reward it's getting this will come up a
little bit later in the context of go
which we'll talk about in a later
tutorial another example of a supervised
learning problem is machine translation
so I may give you a sentence in English
and I might want your algorithm to learn
how to translate this to a sentence in
Spanish traditionally this has been a
very difficult task for supervised
learning and machine learning algorithms
but as we'll also talk about later
there's been actually a great deal of
success in this recently and one thing
you might hear in the context of machine
translation and similar tasks is
sequence learning sequence learning just
means that the thing that you're mapping
from or to is a long sequence of
something like for example it could be a
long sequence of words as in language
and you will also hear the terms
recurrent neural networks and things
like
short-term memory networks LST M's in
conjunction with sequence learning so
these are current neural network sirs
the reason that there's been a lot of
advances in language translation
recently actually finally the last
example I'll talk about is something
like a recommendation system so we might
want to take the set of songs that
you've listened to on some kind of music
listening up and now suggest a new song
that we think you'll like based on what
you've listened to you probably are all
familiar with these examples and they
actually can work surprisingly well in
recommending things you actually end up
liking so that's another example of a
supervised learning problem okay
so one of the last things I want you to
remember and take away from this
tutorial is that there's many many
algorithms that are doing supervised
learning here on the screen you see just
a small subset of these in recent times
you might have heard a lot about neural
networks or deep learning which are
actually an example of neural networks
you should remember that neural networks
are just one example of a machine
learning algorithm momentum among many
others they have like the other
algorithms their strengths and
weaknesses but we'll also talk later
about some of the reasons why they might
be so prevalent recently now in the next
tutorial I'll talk about a spatial or
geometrical way to understand how
machine learning actually does what it
does