Okay, so we've got these three little bits of machine learning here.
And there are a lot of tools and techniques that are inside that.
>> Mm-hm. >> And I think that's great.
And we're going to be trying to teach you a lot of those tools and
techniques and sort of ways to connect them together.
So by the way, as Michael was pointing out,
there are kind of ways that these things might help each other,
unsupervised learning might help supervised learning.
It's actually much deeper than that.
It turns out you, even though unsupervised learning is clearly not
the same as supervised learning at the level that we described it,
in some ways they're exactly the same thing.
Supervised learning you have some bias.
Oh, it's a quadratic function, induction make sense.
All these kind of assumptions you make.
And in unsupervised learning, I told you that we don't know whether this clusters
is better than this cluster, dividing by sex is better than dividing by height,
or, or hair color or whatever.
>> Mm.
>> But ultimately you make some decision about how to cluster, and
that means implicitly there's some assume signal.
There's some assume set of labels that you think make sense.
Oh, I think things that look alike should somehow be clustered together.
>> Mm.
>> Or things that are near one another should cluster together.
So in some ways it's still kind of like supervised learning.
You can certainly turn any
supervised learning problem into an unsupervised learning problem.
>> Mm, mm. >> Right?
So in fact, all of these problems are really the same kind of problem.
>> Yeah, well there's two things that I'd want to add to that.
One is that in some sense, in many cases,
you can formulate all these different problems as some form of optimization.
In supervised learning you want something that,
that labels data well and so you're, the thing you're trying to optimize is
find me a function that, that does that scores it.
In reinforcement learning we're trying to find a behavior that scores well.
And unsu, unsupervised learning we usually have to make up some kind of
a criterion, and then we find a way of clustering the data,
organizing the data so that it scores well.
So that was the first point I wanted to make.
The other one is if you divide things by sex and
your a virgin then there's numerical instability issues.
>> Do you learn about that on the street?
>> I learned it in a Math book.
>> Yes you [NOISE] I'm, I'm going to move on And so here's the thing.
>> All right. >> Everything just Michael just
said except the last part is true.
But there's actually a sort of deeper thing going on here.
To me, if you think about the commonalities of everything we've just
said, it boils down to one thing data, data, data, data, data, data.
Data is king in machine learning.
Now Michael would call himself a computer scientist.
>> Oh, yeah. >> And I would call
myself a computationalist.
>> What? >> What if I'm in a college of
computing at a department of computer science?
I believe in computing and computation as being the ultimate thing.
So I would call myself a computationalist, and
Michael would probably agree with that just to keep this discussion moving.
>> Let's say.
>> Right.
So we're computationalists.
We believe in computing.
That's a good thing. >> Sure.
>> Many of our colleagues who do computations tend to think in terms of
algorithms.
They think in terms of what are the series of steps I
need to do in order to solve some problem?
Or they.
>> [CROSSTALK]. >> Might think in terms of theorems.
If I try to describe this problem in a particular way,
is it solvable quizzically by some algorithm?
>> Yeah.
>> And, truthfully, machine learning is a lot of that.
But the difference between the person who's trying to solve our problem as
an AI person or as a computing person and
somebody who's trying to solve our problem as a machine learning person is
that the algorithm stops being central, the data starts being central.
And so what I hope you get out of this class, or at least part of the stuff
that you do, is understanding that you have to believe the data,
you have to do something with the data, you have to be consistent with the data.
The algorithms that fall out of all that are algorithms, but
they're algorithms that take the data as primary or at least important.
>> I'm going to go with co-equal.
>> So the algorithms and data are co-equal.
>> Co-equal.
>> Well if you believe in Lisp, they're the same thing.
>> Exactly!
>> All right. So there you go.
>> They knew back in the 70s.
>> So it turns out we do agree on most things.
>> [NOISE] That was close.
>> Excellent!
So, the rest of the semester will go exactly like this.
>> [LAUGH]. >> [LAUGH] Except you won't see us.
You'll see our hands though.
>> This side.
This side.
>> You'll see our hands, though.
Thank you, Michael.
>> It's all right.
>> [LAUGH] What?
[LAUGH].
>> What? [LAUGH].
>> That was good, that took me back to when I was four.
Okay, so.
>> Senor Wences.
>> Hm? >> It's called Señor Wences.
>> Yes, I know.
>> Yeah, okay. >> I remember that.
>> Mm-hm, yeah. >> I'm not
that much younger than you are.
>> Little bit.
>> Ten, 12 years only.
>> No come on.
>> You can count gray hairs.
Anyway the point is the rest of the semester will go like this.
We will talk about supervised learning and a whole series of algorithms.
Step back a little bit and talk about the theory behind them, and try to
connect theory of machine learning with theory of computing notions, or
at least that kind of basic idea.
What does it mean to be a hard problem versus an easier problem?
Will move into randomized optimization and unsupervised learning where we
will talk about all the issues that we brought up here and try to connect them
back to some of the things that we did in the section on supervised learning.
And then finally, we will spend our time on reinforcement learning.
And a generalization of these traditional reinforcement learning,
which involves multiple agents.
So we'll talk about a little bit of.
>> Mm-hm. >> Game theory,
which Michael loves to talk about.
I love to talk about.
And the applications of all the stuff that we've been learning to
solving problems of how to actually act in the world?
How to build that world out to do something?
Or build that agent to play a game or
to teach you how to do whatever you, you need to be taught how to do?
But at the end of the day, we're going to teach you how to
think about data, how to think about algorithms, and
how to build artifacts that you know, will learn?
>> Let's do this thing.
>> Excellent. All right.
Well thank you Michael.
>> Sure.
>> I will see you next time we're in the same place at the same time.