>> What about unsupervised learning?
>> Right, so unsupervised learning we don't get those
examples. We have just essentially something like inputs and
we have to derive some structure from them just
by looking at the relationship between the inputs themselves.
>> Right, so give me an example of that.
>> So, when you are studying different kinds of animals say, even as a kid.
>> Mm-hm.
>> You might start to say oh, there's these animals that all look kind of the
same, they're four legged. I'm going to call all
of them them dogs. Even if they happen to
be horses or cows or whatever, but I
have developed, without anyone telling me, this sort
of notion that all these belong into the
same class and it's different from things like trees.
>> Which don't have 4 legs.
>> Well some do, but I mean they have, they both bark, is all I'm saying.
>> [LAUGH] Did I really set you up for that?
>> Not on purpose.
>> I'm sorry, I want to
apologize to each and every one of you for that. But that was pretty good.
Michael is very good at word play, which I guess is often unsupervised as well.
>> No, I get a lot of supervision! [LAUGH]
>> You certainly get a lot of feedback.
>> Yeah, that's right. It's like, please stop doing that.
>> So if supervised learning is about function approximation,
then unsupervised learning is about description. It's about taking
a set of data and figuring out how you
might divide it up in one way or the other.
>> Or maybe even summarization. It's not just
a description, but it's a shorter description.
>> Yeah, it's usually a concise. Compression.
>> Compact, description. So I might take a bunch
of pixels like I have here, and might say, male.
>> [LAUGH] Wait, wait, wait, wait. I am pixels now?
>> As far as we can tell.
>> That's fine.
>> I however, am not pixels. I know I am not
pixels. I am pretty sure that rest of you are pixels.
>> That's right.
>> So, I have a bunch of pixels and I might say, male. And or I
might say female, or I might say dog, or I might say tree, but the point
is, I don't have a bunch of labels that say
dog, tree, male or female. I just decide that pixels
like this belong with pixels like this, as opposed to
pixels like something else that I'm pointing to behind me.
>> Yeah, we're living in a world right now
that is devoid of any other objects. Oh, chairs.
>> Chairs right?
>> Chairs.
>> So these pixels are very different from those pixels, because of where
they are relative to the other pixels, say. Right? So if you were looking.
>> I'm not sure that's helping me understand unsupervised learning.
>> Go out and, go outside and
look at a crowd of people and try to decide how you might divide
them up. Maybe you'll divide them up
by ethnicity, maybe you'll divide them up by
whether they have purposely shaven their hair in order to mock the bald, or
whether they have curly hair. Maybe you'll
divide them up by whether they have goatees.
>> Facial hair.
>> Or whether they have grey hair. There's lots
of things that you might do in order to
>> Did you just point at me and say grey hair?
>> I was pointing and your head happened to be there.
>> Pixels it's, its a two dimensional.
>> Oh, come on, where's the
grey hair?
>> Right there. It's right where your spit curl is.
>> All right.
>> Okay, so, imagine you're dividing the world up that way. You could divide
it up male/female, you could divide it
up short/tall, wears hats/doesn't wear hats. All kinds
of ways you can divide it up, and no one's telling you the right way
to divide it up, at least not
directly. That's unsupervised learning, that's description, because now,
>> Mm.
>> Rather than having to send pixels of everyone, or having
to do a complete description of this crowd, you can say there
were 57 males, and 23 females, say. Or,
there were mostly people with beards, or whatever.
>> I like summarization for that.
>> I like summarization for that, it's
a nice concise description. That's unsupervised learning.
>> > Good, very good. And it's different than supervised learning.
>> In fact. It's different than supervised learning. And it's
different in a couple of ways. One way that it's different
is, all of those ways that we could have just
divided up the world, in some sense they're all equally good.
So I could divide up by sex. Or I could divide up by height. Or I can
divide up by clothing or whatever and they're
all equally good, absent some other signal later telling
you how you should be dividing up the
world. But supervised learning directly tells you there's a
signal, this is what it ought to be,
and that's how you train. Those are very different.
>> Now, but I can ways that unsupervised
learning could be helpful in the supervised setting.
Alright, so if I do get a nice
description and it's the right kind of description,
it might help me map to, it might help me do the function approximation better.
>> Right. So instead of taking pixels at input, as
input and then labels like male or female, I can
just simply take a summarization of you like, how much
hair you have, your relative height to weight. And various things
like that that might help me do it. That's right.
And by the way, in practice, this tends to turn out
to be things like density estimation. We do end up
turning it into statistics at the end of the day. Often.
>> It was statistics from the beginning. But when you say
density estimation.
>> Yes.
>> Are you saying I'm stupid?
>> No.
>> Alright, so what is density estimation?
>> Well, they'll have to take the class to find out.
>> I see.
>> Okay.
教師なし学習についてはどうですか？
教師なし学習には訓練例がなく
あるのはインプットなど必要なものだけです
何らかの構造を導き出すために
インプットそのものの関係性を調べるのです
では例を挙げていただけますか
例えば異なる動物について学び始めたとしましょう
子供でさえも動物は外見上似ていると すぐに気がつきます
どの動物も4本足ですから 私はすべての動物を犬と呼びます
馬でも牛でもです
私は誰に教えられなくても これらは同種だと判断できます
木などとは異なっていると
木には4本足はないですね
ありますよ でも問題は吠えるかどうかです
私の誘導に乗りましたね
まんまと
わざと4本足の木の話をしたんです
マイケルは言葉遊びが大好きです
まさか上司にも？
言ってますよ 大勢の上司に
フィードバックも多そうですね
多すぎて困っています
さて教師あり学習が関数近似だとしたら
教師なし学習は説明についての学習です
ひとまとまりのデータを いろいろな方法で分析します
要約とも言えます 短くまとめた説明です
通常は簡潔に要約した説明ですね
私はここにある画素を要約して男性と表現します
何ですって 私は画素ですか
画面上ではね
いいでしょう
でも私は画素ではありません
私以外は画素です
私は画素を見て 男性や女性や犬や木だと言うかもしれません
しかし犬や木や男性や女性というラベルがなければ
ただこんな感じのものだと表現するしかありません
あちらの物とは違うと言えるだけです
ここにある物だけで考えてみましょう
イスです
確かにイスです
こちらの画素がそちらの画素は
相対的に違うのです
ですから
まだ教師なし学習を理解できません
では外に出掛けて人々を観察し
実際に分類してみましょう
分類の基準は民族でもいいですし
薄毛をごまかすために頭を剃っているとか
髪がウェーブがかっているかとか
あごヒゲがあるかでも分類できます
産毛でも
白髪のあるかでもいいですね
方法はたくさんあります
白髪のところで私を指しましたね
たまたま白髪が見えたんです
本当ですか？ どこに白髪が？
そこですよ 髪の分け目のところにあります
今度は世界を分類しましょう
性別や身長や帽子をかぶっているかいないかでも
何でもいいのです
正しい分類の仕方などありません
これが教師なし学習つまり説明です
分類した個人の画素を送ったり 集団に完璧な説明をつけることより
男性は57人で女性は23人だとか
ほとんどの人にヒゲがあったとか言えばいいのです
いい要約ですね
明解で簡潔な説明こそが教師なし学習です
教師あり学習とは異なりますね
実際いくつかの点で異なります
相違点の例として
世界を分類する方法はすべて
ある意味どれも同じなのです
ですから分類の方法は性別でも身長でも
服装でも何でもいいのです
どの方法も等しく有効ですし
不都合があれば分類を変えればいいのです
つまり教師あり学習では 直接伝えられたことをやるだけです
これが訓練になるのです
学習方法は異なりますが 教師なし学習は教師あり学習に役立ちそうですね
分かりやすくて正しい説明があれば
よりよく関数を近似できますからね
そうですね 性別を分類するのに 画素をインプットする代わりに
髪の量でも身長と体重の比でも 何でも好きな要約量を使えるわけです
ところで実際にはこれは密度推定になります
最終的に統計になることもよくあります
最初から統計なのに密度推定ですか？
はい
私はバカですか？
いいえ
では密度推定とは何ですか？
これから学習します
分かりました
ではまた