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ERIC GRIMSON: OK.
Welcome back.
You know, it's that
time a term when
we're all kind of doing this.
So let me see if I can get a few
smiles by simply noting to you
that two weeks from
today is the last class.
Should be worth at least a
little bit of a smile, right?
Professor Guttag is smiling.
He likes that idea.
You're almost there.
What are we doing for the
last couple of lectures?
We're talking about
linear regression.
And I just want to
remind you, this

Turkish: 
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Avukat lisansı.
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ERIC GRIMSON: Tamam.
Tekrar hoşgeldiniz.
Bilirsin işte bu
süre ne zaman
hepimiz bunu yapıyoruz.
Bakalım bir kaç tane alabilir miyim
sadece size not vererek gülümser
o iki hafta
bugün son sınıftır.
En azından bir değer olmalı
Biraz gülümse, değil mi?
Profesör Guttag gülümsüyor.
Bu fikri sever.
Neredeyse oradasın.
Ne yapıyoruz
son dersler?
Hakkında konuşuyoruz
doğrusal regresyon.
Ve sadece istiyorum
sana bunu hatırlatmak

Turkish: 
benim fikrimdi
bazı deneysel veriler.
Koyduğum bir bahar davası
ölçüye göre farklı ağırlıklar
değiştirmeler.
Ve regresyon veriyordu
bize bir model çıkarmanın bir yolu
bu verilere uyacak şekilde.
Ve bazı durumlarda kolaydı.
Örneğin biliyorduk
doğrusal bir model olacak.
En iyi çizgiyi bulduk
bu veriye uyuyordu.
Bazı durumlarda
doğrulama kullanabiliriz
aslında araştırmamıza izin vermek
en iyi modeli bulmak için
uygun olup olmadığını
doğrusal, ikinci dereceden bir kübik,
bazı yüksek dereceli bir şey.
Yani biz onu kullanacağız
Bir model hakkında bir şeyler çıkarmak.
Bu güzel bir segue içine
sonraki üç konu
Son büyük dersler
sınıfın konusu,
bu makine öğrenmesidir.
Ve ben tartışacağım, yapabilirsin
Bunun gerçekten olup olmadığını tartışma
öğrenme örneği.
Ama birçoğu var
bizler
ne zaman konuşmak istiyoruz
Makine öğrenmesi hakkında konuşun.
Yani her zamanki gibi
bir okuma ödevi.
Kitabın 22. Bölümü
Bu konuda iyi bir başlangıç.

English: 
was the idea of I have
some experimental data.
Case of a spring where I put
different weights on measure
displacements.
And regression was giving
us a way of deducing a model
to fit that data.
And In some cases it was easy.
We knew, for example, it was
going to be a linear model.
We found the best line
that would fit that data.
In some cases, we said
we could use validation
to actually let us explore
to find the best model that
would fit it, whether a
linear, a quadratic, a cubic,
some higher order thing.
So we'll be using that to
deduce something about a model.
That's a nice segue into
the topic for the next three
lectures, the last big
topic of the class,
which is machine learning.
And I'm going to argue, you can
debate whether that's actually
an example of learning.
But it has many of
the elements that we
want to talk about when we
talk about machine learning.
So as always, there's
a reading assignment.
Chapter 22 of the book gives
you a good start on this,

English: 
and it will follow
up with other pieces.
And I want to start
by basically outlining
what we're going to do.
And I'm going to
begin by saying,
as I'm sure you're aware,
this is a huge topic.
I've listed just five
subjects in course six
that all focus on
machine learning.
And that doesn't
include other subjects
where learning is
a central part.
So natural language processing,
computational biology,
computer vision
robotics all rely today,
heavily on machine learning.
And you'll see those in
those subjects as well.
So we're not going to
compress five subjects
into three lectures.
But what we are going to do
is give you the introduction.
We're going to start by talking
about the basic concepts
of machine learning.
The idea of having examples, and
how do you talk about features
representing those
examples, how do
you measure distances
between them,
and use the notion
of distance to try
and group similar
things together as a way
of doing machine learning.
And we're going to
look, as a consequence,
of two different standard
ways of doing learning.

Turkish: 
ve takip edecek
diğer parçaları ile.
Ve başlamak istiyorum
temelde ana hatlarıyla
ne yapacağız.
Ve ben gidiyorum
diyerek başlayın,
bildiğinden emin olduğum gibi
bu çok büyük bir konudur.
Sadece beş tane listeledim
6. dersteki konular
tüm odaklanmak
makine öğrenme.
Ve bu değil
diğer konuları dahil et
öğrenme nerede
merkezi bir bölüm.
Doğal dil işleme,
hesaplamalı biyoloji,
Bilgisayar görüşü
robotiklerin hepsi bugün
ağırlıklı olarak makine öğrenmesi üzerine.
Ve bunları göreceksiniz
Bu konular da.
Yani gitmeyeceğiz
beş konu sıkıştır
üç derste.
Ama ne yapacağız
size tanıtım yapmak.
Konuşarak başlayacağız
temel kavramlar hakkında
Makine öğrenmesi.
Örnek alma fikri ve
özellikler hakkında nasıl konuşursun
bunları temsil eden
örnekler, nasıl
mesafeleri ölçtün
onların arasında,
ve kavramı kullanın
denemek için mesafe
ve grup benzer
bir şey olarak birlikte işler
makine öğrenmesi.
Ve biz gidiyoruz
sonuç olarak bak
iki farklı standart
öğrenme yapmanın yolları.

Turkish: 
Bir, biz ararız
sınıflandırma yöntemleri
Örnek biz
görecek, orada
denilen bir şey
"en yakın komşu k"
ve ikinci sınıf,
kümeleme yöntemleri denir.
Sınıflandırma çalışmaları
iyi ne zaman bende
etiketli verileri çağırırız.
Etiketleri biliyorum
örnekler ve ben
bunu kullanacak
sınıfları dene
öğrenebileceğimi ve
kümeleme iyi çalışıyor,
etiketli verilerim olmadığında.
Ve ne olduğunu göreceğiz
birkaç dakika içinde demektir.
Ama biz vereceğiz
bu konuda erken bir görüş.
Profesör Guttag sürece
fikrini değiştirir,
muhtemelen gitmeyeceğiz
sana gerçekten akımı göster
gelişmiş makine
öğrenme yöntemleri
evrişimli sinir gibi
ağlar veya derin öğrenme
okuyacağın şeyler
haberlerde hakkında.
Ama sen gidiyorsun
ne olduğunu anlamak
bunların arkasında, bakarak
ne yaptığımız zaman
öğrenme algoritmaları hakkında konuşur.
Yapmadan önce istiyorum
sana işaret etmek
Bunun ne kadar yaygın olduğu.
Ve itiraf edeceğim
gri saçımla
AI’da çalışmaya başladım.
1975'te makine öğrenmesiyken
yapılacak oldukça basit bir şey.
Ve oldu
izlemek büyüleyici
40 yılda değişim.

English: 
One, we call
classification methods.
Example we're
going to see, there
is something called
"k nearest neighbor"
and the second class,
called clustering methods.
Classification works
well when I have what
we would call labeled data.
I know labels on my
examples, and I'm
going to use that to
try and define classes
that I can learn, and
clustering working well,
when I don't have labeled data.
And we'll see what that
means in a couple of minutes.
But we're going to give
you an early view of this.
Unless Professor Guttag
changes his mind,
we're probably not going to
show you the current really
sophisticated machine
learning methods
like convolutional neural
nets or deep learning,
things you'll read
about in the news.
But you're going to
get a sense of what's
behind those, by looking
at what we do when we
talk about learning algorithms.
Before I do it, I want
to point out to you
just how prevalent this is.
And I'm going to admit
with my gray hair,
I started working in AI in
1975 when machine learning was
a pretty simple thing to do.
And it's been
fascinating to watch
over 40 years, the change.

English: 
And if you think about it, just
think about where you see it.
AlphaGo, machine learning based
system from Google that beat
a world-class level Go player.
Chess has already been conquered
by computers for a while.
Go now belongs to computers.
Best Go players in the
world are computers.
I'm sure many of
you use Netflix.
Any recommendation
system, Netflix,
Amazon, pick your favorite, uses
a machine learning algorithm
to suggest things for you.
And in fact, you've probably
seen it on Google, right?
The ads that pop
up on Google are
coming from a machine
learning algorithm that's
looking at your preferences.
Scary thought.
Drug discovery, character
recognition-- the post office
does character recognition of
handwritten characters using
a machine learning algorithm
and a computer vision system
behind it.
You probably don't
know this company.
It's actually an MIT
spin-off called Two Sigma,
it's a hedge fund in New York.
They heavily use AI and
machine learning techniques.

Turkish: 
Ve eğer düşünürsen, sadece
nerede gördüğünü düşün.
AlphaGo, makine öğrenmeye dayalı
Google'dan yenen sistem
dünya standartlarında seviye Go oyuncu.
Satranç zaten fethedildi
bir süre bilgisayarlar tarafından.
Git şimdi bilgisayarlara ait.
En iyi Go oyuncuları
dünya bilgisayardır.
Eminim çoğundan
Netflix'i kullanıyorsun.
Herhangi bir tavsiye
sistem, Netflix,
Amazon, favorini seç, kullanır
bir makine öğrenme algoritması
senin için bir şeyler önermek için.
Ve aslında, muhtemelen
Google’da gördüm, değil mi?
Açılan reklamlar
Google’da
bir makineden geliyor
öğrenme algoritması bu
tercihlerine bakmak.
Korkunç düşünce
İlaç keşfi, karakter
Tanıma-- postane
karakter tanıma yapar
el yazısı karakterleri kullanarak
bir makine öğrenme algoritması
ve bir bilgisayarlı görü sistemi
arkasında.
Muhtemelen yapmazsın
bu şirketi biliyorum.
Bu aslında bir MIT
İki Sigma denilen spin-off,
New York'ta bir hedge fonudur.
AI ve yoğun olarak kullanıyorlar
makine öğrenmesi teknikleri.

English: 
And two years ago, their
fund returned a 56% return.
I wish I'd invested in the fund.
I don't have the kinds
of millions you need,
but that's an impressive return.
56% return on your
money in one year.
Last year they didn't
do quite as well,
but they do extremely well using
machine learning techniques.
Siri.
Another great MIT
company called Mobileye
that does computer vision
systems with a heavy machine
learning component that is
used in assistive driving
and will be used in
completely autonomous driving.
It will do things like
kick in your brakes
if you're closing too fast
on the car in front of you,
which is going to
be really bad for me
because I drive
like a Bostonian.
And it would be
kicking in constantly.
Face recognition.
Facebook uses this,
many other systems
do to both detect
and recognize faces.
IBM Watson-- cancer diagnosis.
These are all just
examples of machine
learning being used everywhere.
And it really is.
I've only picked nine.

Turkish: 
Ve iki yıl önce, onların
fon% 56 getiri sağladı.
Keşke fona yatırım yapsaydım.
Bende çeşit yok
İhtiyacınız olan milyonlarca
ama bu etkileyici bir geri dönüş.
% 56 getiri
bir yılda para.
Geçen sene yapmadılar
Oldukça iyi
ama son derece iyi kullanıyorlar
makine öğrenmesi teknikleri.
Siri.
Başka bir büyük MIT
Mobileye adlı şirket
bilgisayar vizyonu mu
ağır makineli sistemler
öğrenme bileşeni
yardımcı sürüşlerde kullanılır
ve kullanılacak
tamamen özerk sürüş.
Gibi şeyler yapacak
frenlerine bas
eğer çok hızlı kapanıyorsan
önünüzdeki arabada,
hangisi olacak
benim için gerçekten kötü ol
çünkü ben sürüyorum
Bostonlı gibi.
Ve olurdu
sürekli tekmelemek.
Yüz tanıma.
Facebook bunu kullanıyor
diğer birçok sistem
ikisini de tespit etmek
ve yüzleri tanımak.
IBM Watson, kanser teşhisi.
Bunların hepsi sadece
makine örnekleri
her yerde kullanıldığını öğrenme.
Ve gerçekten öyle.
Sadece dokuz tane seçtim.

Turkish: 
Peki bu nedir?
Bir yapacağım
iğrenç ifade.
Şimdi buna alışkınsın.
İddia edeceğim
yapabileceğini
hemen hemen her bilgisayarın
program bir şeyler öğrenir.
Fakat öğrenme seviyesi
Gerçekten çok değişir.
Öyleyse geri dönmeyi düşünüyorsan
60001’deki ilk ders,
Newton'un metodunu gösterdik
kare kökleri hesaplamak için.
Ve tartışabilirsin.
uzatmak zorunda kalacaksın
ama tartışabilirsin
bu yöntemin öğrendiğini
nasıl yapılır hakkında
karekök hesaplar.
Aslında genelleştirebilirsin
Herhangi bir emir gücünün köklerine.
Ama gerçekten öğrenmedi.
Gerçekten programlamak zorunda kaldım.
Tamam.
Geçen haftayı düşünün.
Doğrusal regresyon hakkında konuştu.
Şimdi hissetmeye başlıyor
biraz daha
Bir öğrenme algoritması gibi.
Çünkü ne yaptık?
Sana bir set verdik
Veri noktalarının
kütle yer değiştirme veri noktaları.
Ve sonra size nasıl gösterdik
bilgisayar aslında olabilir
bu veri noktasına bir eğri sığdırır.
Ve bir anlamda,
bu veri için bir model öğrenme
o zaman kullanabileceğini
davranışını tahmin etmek için.

English: 
So what is it?
I'm going to make an
obnoxious statement.
You're now used to that.
I'm going to claim
that you could
argue that almost every computer
program learns something.
But the level of learning
really varies a lot.
So if you think back to
the first lecture in 60001,
we showed you Newton's method
for computing square roots.
And you could argue,
you'd have to stretch it,
but you could argue
that that method learns
something about how to
compute square roots.
In fact, you could generalize
it to roots of any order power.
But it really didn't learn.
I really had to program it.
All right.
Think about last week when we
talked about linear regression.
Now it starts to feel
a little bit more
like a learning algorithm.
Because what did we do?
We gave you a set
of data points,
mass displacement data points.
And then we showed you how
the computer could essentially
fit a curve to that data point.
And it was, in some sense,
learning a model for that data
that it could then use
to predict behavior.

Turkish: 
Diğer durumlarda
Ve bu oluyor
neye daha yakın
ne zaman isteriz
bir makine düşün
öğrenme algoritması.
Bunu programlamak isteriz
Deneyimden öğrenebilir,
yapabileceği bir şey
yeni gerçekleri ortaya çıkarmak için kullanın.
Şimdi bir sorun oldu
Çok uzun süre AI.
Ve bu teklifi seviyorum.
Bir beyefendiden
Sanat Samuel adlı.
1959 alıntı
İçinde söylediği
onun tanımı
makine öğrenme
çalışma alanı
bu bilgisayarlar verir
olmadan öğrenme yeteneği
açıkça programlanmış olmak.
Ve bence çok
insanlar tartışacak
ilk böyle bir programı yazdı.
Deneyimden öğrendi.
Bu durumda, dama oynadı.
Bir çeşit size nasıl gösterir
alan ilerledi.
Ama dama ile başladık.
satranç yapmalıyız, şimdi gidiyoruz.
Ama dama oynadı.
Ulusal seviyeyi geçti
oyuncular, en önemlisi,
öğrendi
yöntemlerini geliştirmek
oyunlarda nasıl yaptığını izleyerek
ve sonra bir şey çıkarsama
ne düşündüğünü değiştirmek
Bunun yaptığı gibi.
Samuel bir demet yaptı
başka şeylerden.

English: 
In other situations.
And that's getting
closer to what
we would like when we
think about a machine
learning algorithm.
We'd like to have program that
can learn from experience,
something that it can then
use to deduce new facts.
Now it's been a problem in
AI for a very long time.
And I love this quote.
It's from a gentleman
named Art Samuel.
1959 is the quote
in which he says,
his definition of
machine learning
is the field of study
that gives computers
the ability to learn without
being explicitly programmed.
And I think many
people would argue,
he wrote the first such program.
It learned from experience.
In his case, it played checkers.
Kind of shows you how
the field has progressed.
But we started with checkers,
we got to chess, we now do Go.
But it played checkers.
It beat national level
players, most importantly,
it learned to
improve its methods
by watching how it did in games
and then inferring something
to change what it thought
about as it did that.
Samuel did a bunch
of other things.

English: 
I just highlighted one.
You may see in a
follow on course,
he invented what's called
Alpha-Beta Pruning, which
is a really useful
technique for doing search.
But the idea is, how can
we have the computer learn
without being
explicitly programmed?
And one way to
think about this is
to think about the difference
between how we would normally
program and what we would
like from a machine learning
algorithm.
Normal programming, I
know you're not convinced
there's such a thing
as normal programming,
but if you think of
traditional programming,
what's the process?
I write a program that
I input to the computer
so that it can then
take data and produce
some appropriate output.
And the square root finder
really sits there, right?
I wrote code for using Newton
method to find a square root,
and then it gave me the
process of given any number,
I'll give you the square root.
But if you think about
what we did last time,
it was a little different.
And in fact, in a machine
learning approach,

Turkish: 
Sadece bir tanesini vurguladım.
Bir görebilirsiniz
dersi takip et,
O ne denir icat etti
Alfa-Beta Budama, hangi
gerçekten yararlı
arama yapmak için teknik.
Fakat fikir şu ki, nasıl olabilir
bilgisayarın öğrenmesini sağladık
olmadan
açıkça programlanmış mı?
Ve bir yolu
bunun hakkında düşün
farkı düşünmek
normalde nasıl olurduk
program ve ne yapardık
Bir makine öğrenmesinden olduğu gibi
algoritması.
Normal programlama, ben
ikna olmadığını biliyorum
böyle bir şey var
normal programlama olarak
ama eğer düşünüyorsanız
geleneksel programlama,
süreç nedir
Bir program yazıyorum
Bilgisayara giriş yapıyorum
böylece yapabilir
veri almak ve üretmek
bazı uygun çıktılar.
Ve karekök bulucu
gerçekten orada oturuyor, değil mi?
Newton kullanmak için kod yazdım
karekök bulma yöntemi,
ve sonra bana verdi
Herhangi bir sayının verilmesi,
Sana karekök vereceğim.
Ama düşünürsen
geçen sefer ne yaptık
biraz farklıydı.
Ve aslında, bir makinede
öğrenme yaklaşımı

Turkish: 
fikir şu ki ben gidiyorum
bilgisayar çıktısını vermek.
Ben ona örnekler vereceğim
programın ne yapmasını istiyorum
veri üzerindeki etiketler,
karakterizasyonu
farklı şeyler sınıfları.
Ve ne istiyorum
bilgisayar yapmak
, bu karakterizasyonu verilen
çıktı ve veri
O makineyi istedim
öğrenme algoritması
aslında üretmek
benim için bir program
Yapabileceğim bir program
sonra çıkarsama için kullanın
şeyler hakkında yeni bilgiler.
Ve eğer yaratırsan,
gibi, gerçekten güzel bir döngü
nerede alabilirim
makine öğrenme algoritması
programı öğren
hangisini daha sonra kullanabilirim
başka bir sorunu çözmek için.
Bu gerçekten olurdu
Yapabilirsek harika.
Ve önerdiğim gibi, bu
eğri uydurma algoritması
bunun basit bir versiyonudur.
İçin bir model öğrendi
o zaman yapabildiğim veriler
diğerlerini etiketlemek için kullanın
veri örnekleri
veya ne göreceğimi tahmin et
yay yer değiştirme şartları
kitleleri değiştirdiğim gibi.
Demek bu tür bir fikir
Keşfetmeye gidiyoruz.
Öğrenmek istiyorsak
şeyler, biz de yapabiliriz
sor, peki nasıl öğrenirsin?

English: 
the idea is that I'm going
to give the computer output.
I'm going to give it examples of
what I want the program to do,
labels on data,
characterizations
of different classes of things.
And what I want
the computer to do
is, given that characterization
of output and data,
I wanted that machine
learning algorithm
to actually produce
for me a program,
a program that I can
then use to infer
new information about things.
And that creates, if you
like, a really nice loop
where I can have the
machine learning algorithm
learn the program
which I can then use
to solve some other problem.
That would be really
great if we could do it.
And as I suggested, that
curve-fitting algorithm
is a simple version of that.
It learned a model for the
data, which I could then
use to label any other
instances of the data
or predict what I would see in
terms of spring displacement
as I changed the masses.
So that's the kind of idea
we're going to explore.
If we want to learn
things, we could also
ask, so how do you learn?

English: 
And how should a computer learn?
Well, for you as a human, there
are a couple of possibilities.
This is the boring one.
This is the old style
way of doing it, right?
Memorize facts.
Memorize as many facts as you
can and hope that we ask you
on the final exam
instances of those facts,
as opposed to some other
facts you haven't memorized.
This is, if you think way
back to the first lecture,
an example of declarative
knowledge, statements of truth.
Memorize as many as you can.
Have Wikipedia in
your back pocket.
Better way to learn is to
be able to infer, to deduce
new information from old.
And if you think
about this, this
gets closer to what we
called imperative knowledge--
ways to deduce new things.
Now, in the first
cases, we built
that in when we wrote that
program to do square roots.
But what we'd like in
a learning algorithm
is to have much more like
that generalization idea.
We're interested in
extending our capabilities

Turkish: 
Bir bilgisayar nasıl öğrenmeli?
Bir insan olarak senin için orada
birkaç olasılık var.
Bu çok sıkıcı.
Bu eski stil
Bunu yapmanın yolu değil mi?
Gerçekleri ezberle.
Sizin kadar gerçekleri ezberlemek
sana sorabiliriz ve umarız
final sınavında
bu gerçeklerin örnekleri
başkalarına karşı
ezberlemediğin gerçekler.
Bu, eğer böyle düşünüyorsan
ilk derse geri dön
bildirim örneği
bilgi, doğruluk beyanları.
Mümkün olduğu kadar çok ezberleyin.
Wikipedia'da var
arka cebinde
Öğrenmenin daha iyi bir yolu
çıkarım yapabilmek, sonuç çıkarmak
eskiden yeni bilgiler.
Ve eğer düşünüyorsan
bu konuda, bu
neye yaklaşıyoruz
zorunlu bilgi denir
yeni şeyler çıkarmanın yolları.
Şimdi, ilkinde
davalar inşa ettik
Bunu biz yazdığımızda
karekök yapmak için program.
Ama ne istiyoruz
bir öğrenme algoritması
çok daha fazla sahip olmak
bu genelleme fikri.
Biz ilgileniyoruz
yeteneklerimizi genişletmek

Turkish: 
program yazabilmek
yararlı bilgi çıkarımı
örtük
verilerdeki kalıplar.
Yani bir şey değil
açıkça inşa
Bunun karşılaştırması gibi
ağırlıklar ve yer değiştirmeler,
ama aslında örtük
verilerdeki kalıplar,
ve algoritma şekline sahip
bu kalıpların ne olduğunu
ve bunları kullan
size bir program oluşturmak
yeni çıkarım için kullanabilirsiniz
nesneler hakkında veri
dize hakkında
ne olursa olsun
yapmaya çalışıyorsun.
TAMAM.
Öyleyse fikir o zaman,
temel paradigma
gidiyoruz
görmek için biz mi
verecek
biraz eğitim sistemi
veri, bazı gözlemler.
Bunu geçen sefer yaptık.
Sadece bahar yer değiştirmeleri.
Biz o zaman gidiyoruz
dene ve bir yolunu dene
anlamak için nasıl
kod yazarız, nasıl yaparız
program yaz, sistem
bu bir şey çıkartacak
Bu süreç hakkında
veri oluşturuldu?
Ve sonra
öyle olmak istiyoruz
Bunu yapmak için kullanmak mümkün
şeyler hakkında tahminler
daha önce görmedik.
Bu yüzden tekrar istiyorum
eve bu noktaya sür.

English: 
to write programs that can
infer useful information
from implicit
patterns in the data.
So not something
explicitly built
like that comparison of
weights and displacements,
but actually implicit
patterns in the data,
and have the algorithm figure
out what those patterns are,
and use those to
generate a program you
can use to infer new
data about objects,
about string
displacements, whatever
it is you're trying to do.
OK.
So the idea then,
the basic paradigm
that we're going
to see, is we're
going to give the
system some training
data, some observations.
We did that last time with
just the spring displacements.
We're going to then
try and have a way
to figure out, how do
we write code, how do we
write a program, a system
that will infer something
about the process that
generated the data?
And then from
that, we want to be
able to use that to make
predictions about things
we haven't seen before.
So again, I want to
drive home this point.

Turkish: 
Eğer düşünürsen,
bahar örneği bu modele uyar.
Sana bir dizi verdim
veri, mekansal sapmalar
kütle yer değiştirmelerine göre.
Farklı kitleler için nasıl
bahar ne kadar ileri gitti?
Sonra bir şey çıkardım
temel süreç hakkında.
İlk durumda ben
Doğrusal olduğunu biliyorum.
ama ne olduğunu çözmeme izin ver
gerçek lineer denklem.
Bahar sabiti ne
ile ilişkili?
Ve bu sonuca dayanarak,
Bir kod parçam var
Tahmin etmek için kullanabilirim
yeni yer değiştirmeler.
Yani hepsine sahip
elemanlar, eğitim verileri,
bir çıkarım motoru,
ve sonra yetenek
bunu yapmak için kullanmak
Yeni tahminler.
Ama bu çok basit
tür öğrenme ortamı.
Yani daha yaygın
biri ben
olarak kullanacak
bir örnek
sana verdiğimde
bir dizi örnek,
bu örnekler bazı
bunlarla ilgili veriler,
bazı özellikler ve bazı etiketler.
Her örnek için
Bunu söyleyebilirim
özel bir şey.
Bu diğeri
başka bir şey.
Ve ne istiyorum
çözmek

English: 
If you think about it, the
spring example fit that model.
I gave you a set of
data, spatial deviations
relative to mass displacements.
For different masses, how
far did the spring move?
I then inferred something
about the underlying process.
In the first case, I
said I know it's linear,
but let me figure out what
the actual linear equation is.
What's the spring constant
associated with it?
And based on that result,
I got a piece of code
I could use to predict
new displacements.
So it's got all of those
elements, training data,
an inference engine,
and then the ability
to use that to make
new predictions.
But that's a very simple
kind of learning setting.
So the more common
one is one I'm
going to use as
an example, which
is, when I give you
a set of examples,
those examples have some
data associated with them,
some features and some labels.
For each example,
I might say this
is a particular kind of thing.
This other one is
another kind of thing.
And what I want to
do is figure out

Turkish: 
çıkarsama yapmak nasıl
yeni şeyler etiketlemek.
Yani sadece bu değil, ne
kütlenin yer değiştirmesi,
bu aslında bir etiket.
Ve birini kullanacağım
en sevdiğim örnekleri
Ben büyük bir yeni
İngiltere Patriots hayranı,
değilseniz, özür dilerim.
Ama kullanacağım
Futbol oyuncuları
Bu yüzden göstereceğim
bir saniye sonra
Sana bir dizi vereceğim
Futbolcu örnekleri
Etiket
oynadıkları pozisyonda.
Ve veriler, tamam
birçok şey olabilir.
Kullanacağız
yükseklik ve ağırlık.
Ama ne istiyoruz
yapmak sonra görmek
nasıl buluruz
nitelendirmenin bir yolu
örtük kalıbı nasıl
ağırlık ve boy öngörüyor mu
pozisyon türü
Bu oyuncu oynayabilir.
Ve sonra gel
bir algoritma ile
Bu tahmin edecek
yeni oyuncuların pozisyonu.
Taslağı yapacağız
sonraki yıl için.
Nerede oynamasını istiyoruz?
Bu paradigmadır.
Potansiyel gözlemler seti
etiketli, potansiyel olarak değil.
Nasıl yapacağımızı düşün
Bir model bulmak için çıkarım.
Ve sonra bunu nasıl kullanırız?
Tahmin yapma modeli.
Ne gidiyoruz
görmek için ve biz
çoklu görecek
bugün örnekler
bu mu
öğrenme yapılabilir

English: 
how to do inference on
labeling new things.
So it's not just, what's the
displacement of the mass,
it's actually a label.
And I'm going to use one
of my favorite examples.
I'm a big New
England Patriots fan,
if you're not, my apologies.
But I'm going to use
football players.
So I'm going to show
you in a second,
I'm going to give you a set of
examples of football players.
The label is the
position they play.
And the data, well, it
could be lots of things.
We're going to use
height and weight.
But what we want
to do is then see
how would we come up with
a way of characterizing
the implicit pattern of how
does weight and height predict
the kind of position
this player could play.
And then come up
with an algorithm
that will predict the
position of new players.
We'll do the draft
for next year.
Where do we want them to play?
That's the paradigm.
Set of observations, potentially
labeled, potentially not.
Think about how do we do
inference to find a model.
And then how do we use that
model to make predictions.
What we're going
to see, and we're
going to see multiple
examples today,
is that that
learning can be done

Turkish: 
iki çok geniş yollardan birinde.
İlki denir
denetimli öğrenme
Ve bu durumda,
her yeni örnek için
Sana bir parçası olarak veriyorum
Eğitim verilerinin
Üzerinde bir etiket var.
Ne olduğunu biliyorum.
Ve ne gidiyorum
yapmak nasıl yapılır
bir kuralı mı bulabilirim
ilişkili etiketi tahmin et
görünmeyen girdi tabanlı
bu örnekler üzerinde.
Denetlenir çünkü ben
etiketlemenin ne olduğunu biliyorum.
İkinci tür, eğer
bu denetlenir,
bariz diğeri
denetimsiz denir.
Bu durumda, ben sadece gidiyorum
size birkaç örnek vereceğim.
Ama etiketleri bilmiyorum
onlarla ilişkili.
Ben sadece gidiyorum
dene ve ne bul
doğal yollardır
bu örnekleri gruplandırmak
farklı modellerde birlikte.
Ve bazı durumlarda, bilebilirim
Kaç tane model var?
Bazı durumlarda
sadece ne olduğunu söylemek istiyorum
bulabildiğim en iyi gruplama.
TAMAM.
Bugün ne yapacağım
çok fazla kod değil.
Bunun için alkış bekliyordum.
John, ama onları anlamadım.
Çok fazla kod yok.
Ne yapacağım
temelde size göstermek

English: 
in one of two very broad ways.
The first one is called
supervised learning.
And in that case,
for every new example
I give you as part
of the training data,
I have a label on it.
I know the kind of thing it is.
And what I'm going
to do is look for how
do I find a rule that would
predict the label associated
with unseen input based
on those examples.
It's supervised because I
know what the labeling is.
Second kind, if
this is supervised,
the obvious other one
is called unsupervised.
In that case, I'm just going to
give you a bunch of examples.
But I don't know the labels
associated with them.
I'm going to just
try and find what
are the natural ways
to group those examples
together into different models.
And in some cases, I may know
how many models are there.
In some cases, I may
want to just say what's
the best grouping I can find.
OK.
What I'm going to do today
is not a lot of code.
I was expecting cheers for that,
John, but I didn't get them.
Not a lot of code.
What I'm going to do
is show you basically,

English: 
the intuitions behind
doing this learning.
And I"m going to start with my
New England Patriots example.
So here are some data points
about current Patriots players.
And I've got two
kinds of positions.
I've got receivers,
and I have linemen.
And each one is just labeled by
the name, the height in inches,
and the weight in pounds.
OK?
Five of each.
If I plot those on a
two dimensional plot,
this is what I get.
OK?
No big deal.
What am I trying to do?
I'm trying to learn, are
their characteristics
that distinguish the two
classes from one another?
And in the unlabeled
case, all I have
are just a set of examples.
So what I want to
do is decide what
makes two players similar
with the goal of seeing,
can I separate this
distribution into two or more
natural groups.
Similar is a distance measure.
It says how do I take
two examples with values
or features
associated, and we're
going to decide how
far apart are they?

Turkish: 
arkasındaki sezgiler
bu öğrenmeyi yapıyorum.
Ve ben ile başlayacağım
New England Patriots örneği.
Yani burada bazı veri noktaları
Mevcut Patriots oyuncuları hakkında.
Ve bende iki tane var
pozisyon çeşitleri.
Alıcılarım var
ve bende ketenler var.
Ve her biri sadece etiketli
adı, inç cinsinden yüksekliği,
ve kilo olarak kilo.
TAMAM?
Her biri beşi.
Bunları bir arsa üzerinde
iki boyutlu arsa,
Ben böyle alayım.
TAMAM?
Önemli değil.
Ne yapmaya çalışıyorum?
Öğrenmeye çalışıyorum
onların özellikleri
Bu iki ayırt
birbirinden sınıflar?
Ve etiketlenmemiş
dava, sahip olduğum her şey
sadece bir dizi örnek.
Peki ne istiyorum
ne karar ver
iki oyuncuyu benzer yapar
görme amacı ile,
bunu ayırabilir miyim
iki veya daha fazlasına dağıtım
doğal gruplar
Benzer bir mesafe ölçüsüdür.
Nasıl alırım diyor
değerleri olan iki örnek
veya özellikler
ilişkili ve biz
nasıl karar vereceğim
uzaklarda mı?

Turkish: 
Ve etiketlenmemiş durumda,
Bunu yapmanın basit bir yolu,
olduğunu biliyorsam
orada en az k grubu var.
bu durumda gidiyorum
orada olduğunu söylemek için
orada iki farklı grup var.
nasıl karar verdim nasıl
işleri kümelemek için en iyisi
birlikte hepsi bu
bir gruptaki örnekler
birbirlerine yakın
diğer gruptaki örnekler
birbirlerine yakın
Onlar oldukça uzaklar.
Bunu yapmanın birçok yolu var.
Sana bir tane göstereceğim.
Bu çok standart bir yol ve
Temel olarak, aşağıdaki gibi çalışır.
Tek bildiğim bu ise
Orada iki grup var.
Ben başlayacağım
sadece seçerek
örnek olarak iki örnek.
Onları rastgele seç.
Aslında rastgele mükemmel değil.
Bende seçmek istemiyorum
birbirlerine yakın.
Deneyeceğim ve
onları birbirinden ayırın.
Ama iki örnek seçtim
Örneklerim olarak.
Ve diğerleri için
Eğitim verilerinde örnekler,
Hangisini söylüyorum
en yakın mı?
Ne deneyeceğim
ve küme oluşturmaktır
özellikli
mesafeler
tüm örnekler arasında
bu küme küçük.
Ortalama mesafe küçük.

English: 
And in the unlabeled case, the
simple way to do it is to say,
if I know that there are
at least k groups there--
in this case, I'm going
to tell you there are
two different groups there--
how could I decide how
best to cluster things
together so that all the
examples in one group
are close to each other, all
the examples in the other group
are close to each other, and
they're reasonably far apart.
There are many ways to do it.
I'm going to show you one.
It's a very standard way, and
it works, basically, as follows.
If all I know is that
there are two groups there,
I'm going to start
by just picking
two examples as my exemplars.
Pick them at random.
Actually at random is not great.
I don't want to pick too
closely to each other.
I'm going to try and
pick them far apart.
But I pick two examples
as my exemplars.
And for all the other
examples in the training data,
I say which one
is it closest to.
What I'm going to try
and do is create clusters
with the property
that the distances
between all of the examples
of that cluster are small.
The average distance is small.

Turkish: 
Bakalım yapabilir miyim
kümeleri bulmak
ortalama mesafeyi alır
Her iki küme için
mümkün olduğunca küçük.
Bu algoritma tarafından çalışır
iki örnek seçmek,
diğerlerini kümelemek
basitçe söyleyerek örnekler
gruba koymak
bu örneğe en yakın olanı.
Bir zamanlar var
bu kümeler ben
medyanı bulacak
bu grubun elemanı.
Kasten değil ama medyan, ne
merkeze en yakın olanı mı?
Ve bunları örnek olarak kabul edin
ve işlemi tekrarlayın.
Ve ben de yaparım
bazı zamanlar
ya da ben alamadım kadar
süreçteki değişim.
Bu yüzden kümeleme
mesafeye göre.
Ve biz geri döneceğiz
bir saniyedeki mesafe.
Yani burada ne olurdu
futbolcularıma sahibim.
Eğer bunu yeni yapsaydım
ağırlığına göre,
doğal var
Ayırma çizgisi
Ve bir anlam ifade ediyor.
Tamam?
Bu üç
belli ki kümelenmiş,
ve yine
Sadece bu eksende.
Hepsi buradalar.
Bu yedi
farklı bir yer.
Doğal var
orada çizgi bölerek.

English: 
And see if I can
find clusters that
gets the average distance
for both clusters
as small as possible.
This algorithm works by
picking two examples,
clustering all the other
examples by simply saying
put it in the group to which
it's closest to that example.
Once I've got
those clusters, I'm
going to find the median
element of that group.
Not mean, but median, what's
the one closest to the center?
And treat those as exemplars
and repeat the process.
And I'll just do it either
some number of times
or until I don't get any
change in the process.
So it's clustering
based on distance.
And we'll come back to
distance in a second.
So here's what would
have my football players.
If I just did this
based on weight,
there's the natural
dividing line.
And it kind of makes sense.
All right?
These three are
obviously clustered,
and again, it's
just on this axis.
They're all down here.
These seven are at
a different place.
There's a natural
dividing line there.

English: 
If I were to do it based
on height, not as clean.
This is what my
algorithm came up
with as the best
dividing line here,
meaning that these four,
again, just based on this axis
are close together.
These six are close together.
But it's not nearly as clean.
And that's part of the
issue we'll look at
is how do I find
the best clusters.
If I use both
height and weight, I
get that, which was actually
kind of nice, right?
Those three cluster together.
they're near each other,
in terms of just
distance in the plane.
Those seven are near each other.
There's a nice, natural
dividing line through here.
And in fact, that
gives me a classifier.
This line is the
equidistant line
between the centers
of those two clusters.
Meaning, any point
along this line
is the same distance to
the center of that group
as it is to that group.
And so any new example,
if it's above the line,
I would say gets that label,
if it's below the line,
gets that label.
In a second, we'll
come back to look

Turkish: 
Tabanlı yaparsam
Yükseklikte, temiz değil.
Bu ne benim
algoritma geldi
en iyisi ile
burada çizgi bölerek,
Yani bu dört,
tekrar, sadece bu eksene dayanarak
birbirlerine yakınlar.
Bu altı birbirine yakın.
Ama neredeyse o kadar temiz değil.
Ve bu da bir parçası
bakacağımız konu
nasıl bulabilirim
en iyi kümeler.
İkisini de kullanırsam
boy ve kilo, ben
aslında, öyleydi
naziksin, değil mi?
Bu üç küme birlikte.
birbirlerine yakınlar
sadece açısından
düzlemde mesafe.
Bu yedi kişi birbirine yakın.
Güzel bir doğal var
buradan çizgiyi bölerek.
Ve aslında, bu
bana bir sınıflandırıcı verir.
Bu çizgi
eşit hat
merkezler arasında
bu iki kümenin.
Anlamı, herhangi bir nokta
bu çizgi boyunca
aynı
bu grubun merkezi
Bu gruba olduğu gibi.
Ve böylece yeni bir örnek,
çizginin üzerinde ise,
Ben o etiketi alır derdim.
çizginin altındaysa,
bu etiketi alır.
Bir saniye sonra
bakmak için geri dön

Turkish: 
nasıl ölçüyoruz
mesafeler
ama buradaki fikir
oldukça basit.
Gruplandırma bulmak istiyorum
birbirine yakın
ve çok uzak
diğer grup.
Diyelim ki aslında biliyordum
Bu oyuncuların üzerindeki etiketler.
Bunlar alıcılar.
Bunlar ketenler.
Ve sizin için
futbol taraftarları kimlerdir,
Bunu çözebilirsin, değil mi?
Bunlar iki sıkı uç.
Çok daha büyükler.
Bence bu Bennett ve
eğer sen gerçekten Gronk
büyük bir Patriots hayranı.
Ama bunlar sıkı biter
bunlar geniş alıcılardır,
ve olacak
bir saniye sonra gel
ama etiketler var.
Şimdi yapmak istediğim şey söylemek,
eğer faydalanabilirsem
etiketleri bilmek, nasıl
bu grupları bölebilir miyim?
Ve bu görmek kolay.
Bu konuda temel fikir
durum, eğer öyleyse
etiketli gruplar var
bu özellikte
boşluk yapmak istediğim şey
doğal olarak bir yüzey bul
o boşluğu ayırır.
Şimdi yeraltı süslü bir kelimedir.
Diyor ki
iki boyutlu kasa,
bilmek istiyorum
en iyi çizgi nedir

English: 
at how do we measure
the distances,
but the idea here
is pretty simple.
I want to find groupings
near each other
and far apart from
the other group.
Now suppose I actually knew
the labels on these players.
These are the receivers.
Those are the linemen.
And for those of you
who are football fans,
you can figure it out, right?
Those are the two tight ends.
They are much bigger.
I think that's Bennett and
that's Gronk if you're really
a big Patriots fan.
But those are tight ends,
those are wide receivers,
and it's going to
come back in a second,
but there are the labels.
Now what I want to do is say,
if I could take advantage
of knowing the labels, how
would I divide these groups up?
And that's kind of easy to see.
Basic idea, in this
case, is if I've
got labeled groups
in that feature
space, what I want to do is
find a subsurface that naturally
divides that space.
Now subsurface is a fancy word.
It says, in the
two-dimensional case,
I want to know
what's the best line,

Turkish: 
tek bir hat bulabilirsem,
tüm örnekleri ayıran
hepsinden bir etiket ile
ikinci etiket örnekleri.
Bunu göreceğiz, eğer
örnekler iyi ayrılmış,
bu kolay
yapmak ve bu harika.
Ancak bazı durumlarda,
Olacak
daha karmaşık çünkü
bazı örnekler
çok yakın olabilir
bir başkasına.
Ve bu gidiyor
bir problem ortaya çıkarmak
Son dersi gördüğünde.
Fazla giymekten kaçınmak istiyorum.
Oluşturmak istemiyorum
gerçekten karmaşık yüzey
şeyleri ayırmak için.
Ve böylece yapmak zorunda kalabiliriz
birkaç yanlış tolere etmek
etiketli şeyler, eğer
dışarı çıkaramayız.
Ve zaten senin gibi
Bu durumda, anladım
etiketli verilerle,
en uygun çizgi var
tam orada.
280 poundun üzerindeki herkes
Harika bir yan hakem olacak.
280 pound altındaki herkes
alıcı olması daha muhtemeldir.
TAMAM.
Bu yüzden iki farklı var
düşünmeye çalışmanın yolları
Bu etiketlemeyi yapmak hakkında.
Geri geleceğim
ikisi de bir saniye içinde.
Şimdi eklediğimi varsayalım
bazı yeni verilerde.
Yeni örnekleri etiketlemek istiyorum.

English: 
if I can find a single line,
that separates all the examples
with one label from all the
examples of the second label.
We'll see that, if the
examples are well separated,
this is easy to
do, and it's great.
But in some cases,
it's going to be
more complicated because
some of the examples
may be very close
to one another.
And that's going
to raise a problem
that you saw last lecture.
I want to avoid overfitting.
I don't want to create a
really complicated surface
to separate things.
And so we may have to
tolerate a few incorrectly
labeled things, if
we can't pull it out.
And as you already
figured out, in this case,
with the labeled data,
there's the best fitting line
right there.
Anybody over 280 pounds is
going to be a great lineman.
Anybody under 280 pounds is
more likely to be a receiver.
OK.
So I've got two different
ways of trying to think
about doing this labeling.
I'm going to come back to
both of them in a second.
Now suppose I add
in some new data.
I want to label new instances.

Turkish: 
Şimdi bunlar aslında oyuncu
Farklı bir pozisyon
Bunlar geri koşuyorlar.
Ama derim ki, bildiğim kadarıyla
alıcılar ve çamaşırlardır.
Bu iki yeni veri noktasını elde ediyorum.
Bilmek isterdim
olmaları daha muhtemel
Bir alıcı mı yoksa bir astar mı?
Ve veri var
Bu iki bey için.
Yani geri dönersem
Şimdi onları çizmek,
oh sorunlardan birini farkettiniz.
Demek benim askerlerim var
kırmızı olanlar benim alıcılarım
iki siyah nokta
iki çalışan sırt.
Ve tam burada dikkat edin.
Gerçekten olacak
bu ikisini ayırmak zor
birbirinden örnekler.
Birbirlerine çok yakınlar.
Ve bu olacak
şeylerden biri olmak
takas etmeliyiz.
Ama kullanmayı düşünürsem
sınıflandırıcı olarak öğrendiklerimi
etiketlenmemiş veriler ile
iki kümem vardı.
Şimdi görüyorsun, oh
ilginç bir örnek.
Bu yeni örnek
söyle açıkça
yan hakemden daha çok alıcı gibi.
Ama oradaki, belirsiz.

English: 
Now these are actually players
of a different position.
These are running backs.
But I say, all I know about
is receivers and linemen.
I get these two new data points.
I'd like to know, are
they more likely to be
a receiver or a linemen?
And there's the data
for these two gentlemen.
So if I go back to
now plotting them,
oh you notice one of the issues.
So there are my linemen, the
red ones are my receivers,
the two black dots are
the two running backs.
And notice right here.
It's going to be really
hard to separate those two
examples from one another.
They are so close to each other.
And that's going to
be one of the things
we have to trade off.
But if I think about using
what I learned as a classifier
with unlabeled data, there
were my two clusters.
Now you see, oh, I've got
an interesting example.
This new example I would
say is clearly more
like a receiver than a lineman.
But that one there, unclear.

English: 
Almost exactly lies
along that dividing line
between those two clusters.
And I would either say, I
want to rethink the clustering
or I want to say, you know what?
As I know, maybe there
aren't two clusters here.
Maybe there are three.
And I want to classify
them a little differently.
So I'll come back to that.
On the other hand, if I
had used the labeled data,
there was my dividing line.
This is really easy.
Both of those new
examples are clearly
below the dividing line.
They are clearly
examples that I would
categorize as being
more like receivers
than they are like linemen.
And I know it's a
football example.
If you don't like football,
pick another example.
But you get the
sense of why I can
use the data in a labeled
case and the unlabeled case
to come up with different
ways of building the clusters.
So what we're going
to do over the next 2
and 1/2 lectures is
look at how can we
write code to learn that way
of separating things out?
We're going to learn models
based on unlabeled data.
That's the case where I don't
know what the labels are,

Turkish: 
Neredeyse tam olarak yalan
bu çizgi boyunca
bu iki küme arasında.
Ben de derdim ki ben
kümelemeyi yeniden düşünmek istiyorum
ya da söylemek istiyorum ki ne biliyor musun?
Bildiğim gibi belki orada
burada iki küme değil.
Belki üç tane vardır.
Ve sınıflandırmak istiyorum
Onları biraz farklı.
Bu yüzden geri döneceğim.
Öte yandan, eğer
etiketli verileri kullanmıştı
bölme çizgim vardı.
Bu gerçekten kolay.
İkisinin de yeni
örnekler açıkça
Ayırma çizgisinin altında.
Açıkça
yapacağım örnekler
varlık olarak sınıflandırmak
alıcılar gibi
Keten gibiler.
Ve biliyorum ki bir
futbol örneği.
Futbolu sevmiyorsan,
başka bir örnek seç.
Ama sen al
neden yapabileceğimin anlamı
etiketli verileri kullanmak
dava ve etiketlenmemiş dava
farklı gelmek
kümeleri oluşturmanın yolları.
Ne gidiyoruz
sonraki 2 üzerinden yapmak
ve 1/2 ders
nasıl bakalım bakalım
bu şekilde öğrenmek için kod yaz
şeyleri ayırmak?
Modelleri öğreneceğiz
etiketlenmemiş verilere dayanarak.
Benim yapmadığım durum bu
Etiketlerin ne olduğunu bilmek

Turkish: 
sadece yollar bulmaya çalışarak
işleri bir araya getirmek
yakında ve sonra
etiket atamak için kümeler
yeni verilere.
Ve biz modelleri öğreneceğiz
etiketli verilere bakarak
ve en iyi nasıl geldiğimizi görmek
ayrılmanın bir yolu var
bir çizgi veya bir düzlem veya bir
satırların toplanması, örnekler
bir gruptan
diğer grubun örnekleri.
Onayı ile
abartmadan kaçınmak istiyoruz,
oluşturmak istemiyoruz
gerçekten karmaşık bir sistem.
Ve sonuç olarak,
gidiyoruz
biraz yapmak zorunda olmak
ne arasındaki değiş tokuşlar
yanlış pozitif diyoruz
ve yanlış negatifler.
Ancak ortaya çıkan sınıflandırıcı
yeni verileri etiketleyebilir
sadece nereye karar vererek
saygılısın
Bu ayırma çizgisine.
Demek istediğin işte
sonraki 2'yi görmek için
ve 1/2 dersler.
Her makine öğrenmesi yöntemi
beş temel bileşene sahiptir.
Neyin olduğuna karar vermeliyiz
eğitim verileri
ve nasıl değerlendireceğiz
bu sistemin başarısı.
Zaten gördük
bunun bazı örnekleri.
Karar vermeliyiz
nasıl gidiyoruz

English: 
by simply trying to find ways
to cluster things together
nearby, and then use the
clusters to assign labels
to new data.
And we're going to learn models
by looking at labeled data
and seeing how do we best come
up with a way of separating
with a line or a plane or a
collection of lines, examples
from one group, from
examples of the other group.
With the acknowledgment that
we want to avoid overfitting,
we don't want to create a
really complicated system.
And as a consequence,
we're going
to have to make some
trade-offs between what
we call false positives
and false negatives.
But the resulting classifier
can then label any new data
by just deciding where
you are with respect
to that separating line.
So here's what you're going
to see over the next 2
and 1/2 lectures.
Every machine learning method
has five essential components.
We need to decide what's
the training data,
and how are we going to evaluate
the success of that system.
We've already seen
some examples of that.
We need to decide
how are we going

Turkish: 
her örneği temsil etmek
biz veriyoruz.
Yüksekliği seçtim ve
Futbol oyuncuları için ağırlık.
Ama daha iyi olabilirdim
ortalama hız seçmek için kapalı
veya bilmiyorum kol
uzunluk, başka bir şey.
Neyi nasıl çözebilirim
doğru özellikler.
Ve bununla ilişkili
mesafeleri nasıl ölçebilirim
bu özellikler arasında?
Ne olduğuna nasıl karar verebilirim
yakın ve ne yakın değil?
Belki de farklı olmalı
Boy ve boy arasındaki ağırlık terimleri,
Örneğin.
Bu kararı vermem gerekiyor.
Ve bunlar
iki şey biziz
size örnekler göstereceğim
bugün, nasıl geçileceğini.
Gelecek haftadan itibaren
Profesör Guttag
sana nasıl olduğunu gösterecek
Bunları al ve gerçekten başla
daha ayrıntılı sürümler oluşturmak
kümelemenin ölçülmesi,
bulmak için benzerlikleri ölçmek
sizin için nesnel bir işlev
ne olduğuna karar vermek için küçültmek istiyorum
kullanılacak en iyi kümedir.
Ve sonra en iyisi nedir
istediğiniz optimizasyon yöntemi
Bu modeli öğrenmek için kullanmak.
Öyleyse konuşmaya başlayalım
özellikleri hakkında.
Ben bir dizi var
etiketli veya etiketsiz örnekler.
Ne olduğuna karar vermem gerek
bu örnekler hakkında

English: 
to represent each instance
that we're giving it.
I happened to choose height and
weight for football players.
But I might have been better
off to pick average speed
or, I don't know, arm
length, something else.
How do I figure out what
are the right features.
And associated with that,
how do I measure distances
between those features?
How do I decide what's
close and what's not close?
Maybe it should be different, in
terms of weight versus height,
for example.
I need to make that decision.
And those are the
two things we're
going to show you examples of
today, how to go through that.
Starting next week,
Professor Guttag
is going to show you how you
take those and actually start
building more detailed versions
of measuring clustering,
measuring similarities to find
an objective function that you
want to minimize to decide what
is the best cluster to use.
And then what is the best
optimization method you want
to use to learn that model.
So let's start talking
about features.
I've got a set of
examples, labeled or not.
I need to decide what is it
about those examples that's

Turkish: 
ben kullandığımda kullanışlıdır
ne olduğuna karar vermek istiyorum
başka bir şeye yakın ya da değil.
Ve sorunlardan biri
gerçekten kolay olsaydı
gerçekten kolay olurdu.
Özellikler her zaman
ne istersen yakala.
Belabor olacağım
o futbol benzetmesi,
ama neden seçtim
yükseklik ve ağırlık.
Çünkü bulmak kolaydı.
Bilirsin, eğer çalışırsan
New England Patriots, ne
gerçekten olan şey
ne zaman sorarsan
doğru özellik nedir?
Muhtemelen başka biri
şeylerin birleşimi.
Demek tasarımcı olarak
ne demek zorundayım
kullanmak istediğim özellikler.
Bu teklif, tarafından
bir şekilde
büyük istatistikçilerden
20. yüzyılın
Bence onu iyi yakalar.
Yani özellik mühendisliği,
senin gibi, bir programcı olarak,
karar vermek için geliyor
Her ikisi de nelerdir?
Bu vektör içinde ölçmek istiyorum
bir araya getireceğim
ve nasıl karar vereceğim
kilo için göreceli yollar?
Öyleyse John ve Ana ve ben
işimizi yapabilirdi
bu terim gerçekten kolay
eğer otursaydık
başında
terim ve dedi ki,

English: 
useful to use when I
want to decide what's
close to another thing or not.
And one of the problems
is, if it was really easy,
it would be really easy.
Features don't always
capture what you want.
I'm going to belabor
that football analogy,
but why did I pick
height and weight.
Because it was easy to find.
You know, if you work for the
New England Patriots, what
is the thing that you really
look for when you're asking,
what's the right feature?
It's probably some other
combination of things.
So you, as a designer,
have to say what
are the features I want to use.
That quote, by the
way, is from one
of the great statisticians
of the 20th century, which
I think captures it well.
So feature engineering,
as you, as a programmer,
comes down to deciding
both what are the features
I want to measure in that vector
that I'm going to put together,
and how do I decide
relative ways to weight it?
So John, and Ana, and I
could have made our job
this term really easy
if we had sat down
at the beginning of the
term and said, you know,

English: 
we've taught this
course many times.
We've got data
from, I don't know,
John, thousands of students,
probably over this time.
Let's just build a
little learning algorithm
that takes a set of data and
predicts your final grade.
You don't have to
come to class, don't
have to go through
all the problems,
because we'll just
predict your final grade.
Wouldn't that be nice?
Make our job a little easier,
and you may or may not
like that idea.
But I could think about
predicting that grade?
Now why am I telling
this example.
I was trying to see if I
could get a few smiles.
I saw a couple of them there.
But think about the features.
What I measure?
Actually, I'll put this on
John because it's his idea.
What would he measure?
Well, GPA is probably not a
bad predictor of performance.
You do well in other
classes, you're
likely to do well in this class.
I'm going to use this
one very carefully.
Prior programming experience
is at least a predictor,
but it is not a
perfect predictor.
Those of you who haven't
programmed before,
in this class, you can still
do really well in this class.
But it's an indication that
you've seen other programming

Turkish: 
bunu öğrettik
birçok kez ders.
Biz veri var
Bilmiyorum,
John, binlerce öğrenci
Muhtemelen bu süre içinde.
Sadece bir inşa edelim
küçük öğrenme algoritması
bu veri setini alır ve
final notunu tahmin eder.
Zorunda değilsin
sınıfa gel
geçmek zorunda
bütün problemler,
çünkü biz sadece
final notunu tahmin et.
Bu iyi olmaz mıydı?
İşimizi biraz daha kolaylaştırın,
ve sen ya da değil
bu fikir gibi.
Ama düşünebilirdim
o notu tahmin etmek?
Şimdi neden söylüyorum
bu örnek
Ben görmeye çalışıyordum
birkaç gülümsemeye kapılabilirdi.
Orada birkaç tane gördüm.
Ancak özellikleri düşünün.
Neyi ölçtüm?
Aslında, bunu giyeceğim
John bu onun fikri.
Ne ölçecekti?
Eh, not ortalaması muhtemelen bir
performansın kötü tahmincisi.
Başka iyi
dersler sen
Bu sınıfta iyi yapması muhtemel.
Bunu kullanacağım
biri çok dikkatli.
Önceki programlama deneyimi
en azından bir öngörücüdür,
ama bu bir
Mükemmel bir tahmin
Olmayanlar
önceden programlanmış
Bu sınıfta hala yapabilirsin.
Bu sınıfta gerçekten iyi yapın.
Ama bu bir göstergesidir
başka programlama gördün

Turkish: 
duujjil.
Öte yandan, ben yapmam
astrolojiye inan.
Yani ayı sanmıyorum
içinde doğduğun
astrolojik işaret
doğduğun altında
Muhtemelen yapacak bir şeyi vardır.
programın ne kadar iyi.
O göz renginden şüpheliyim
yapacak bir şeyi var
programın ne kadar iyi.
Kaptın bu işi.
Bazı özellikler
önemli, diğerleri yapmaz.
Şimdi sadece hepsini atabilirim
özellikleri ve umuyorum ki
makine öğrenme algoritması
istediklerini sıralar
onlardan uzak durmamak için.
Ama sana bunu hatırlatıyorum
abartma fikri.
Bunu yaparsam,
tehlike var
bazı bulacağını
doğum arasındaki ilişki
ay, göz rengi ve not ortalaması.
Ve bu olacak
bir karara varmak
Gerçekten sevmediğimizi.
Bu arada, durumda
endişelisin
Seni temin ederim
o Stu Schmill
dekanında
kabul departmanı
makine kullanmaz
seni seçmeyi öğreniyorum.
O aslında bir bakar
bir sürü şey
çünkü kolay değil
onu bir makine ile değiştir.
Henüz.
Tamam.

English: 
languages.
On the other hand, I don't
believe in astrology.
So I don't think the month
in which you're born,
the astrological sign
under which you were born
has probably anything to do
with how well you'd program.
I doubt that eye color
has anything to do
with how well you'd program.
You get the idea.
Some features
matter, others don't.
Now I could just throw all
the features in and hope that
the machine learning algorithm
sorts out those it wants
to keep from those it doesn't.
But I remind you of that
idea of overfitting.
If I do that,
there is the danger
that it will find some
correlation between birth
month, eye color, and GPA.
And that's going to
lead to a conclusion
that we really don't like.
By the way, in case
you're worried,
I can assure you
that Stu Schmill
in the dean of
admissions department
does not use machine
learning to pick you.
He actually looks at a
whole bunch of things
because it's not easy to
replace him with a machine--
yet.
All right.

English: 
So what this says is
we need to think about
how do we pick the features.
And mostly, what
we're trying to do
is to maximize something called
the signal to noise ratio.
Maximize those features that
carry the most information,
and remove the ones that don't.
So I want to show
you an example of how
you might think about this.
I want to label reptiles.
I want to come up with a
way of labeling animals as,
are they a reptile or not.
And I give you a single example.
With a single example,
you can't really do much.
But from this example, I know
that a cobra, it lays eggs,
it has scales, it's
poisonous, it's cold blooded,
it has no legs,
and it's a reptile.
So I could say my model
of a reptile is well,
I'm not certain.
I don't have enough data yet.
But if I give you
a second example,
and it also happens
to be egg-laying,
have scales, poisonous,
cold blooded, no legs.
There is my model, right?
Perfectly reasonable
model, whether I design it
or a machine learning
algorithm would

Turkish: 
Peki bu ne diyor
düşünmemiz gerek
özellikleri nasıl seçiyoruz.
Ve çoğunlukla, ne
yapmaya çalışıyoruz
denilen bir şeyi en üst düzeye çıkarmak
sinyal / gürültü oranı.
Bu özellikleri büyüt
en fazla bilgiyi taşımak,
ve yapmayanları da kaldırın.
Bu yüzden göstermek istiyorum
nasıl bir örnek
bunun hakkında düşünebilirsin.
Sürüngenleri etiketlemek istiyorum.
İle gelmek istiyorum
hayvanları etiketleme yolu,
onlar bir sürüngen mi değil mi?
Ve sana tek bir örnek vereyim.
Tek bir örnekle,
Gerçekten fazla bir şey yapamazsın.
Ama bu örnekten biliyorum
bu bir kobra, yumurtlar
ölçekleri var
zehirli, soğuk kanlı
bacakları yok
ve bu bir sürüngen.
Böylece modelimi söyleyebilirim
bir sürüngen
Emin değilim.
Henüz yeterli veri yok.
Ama sana verirseniz
ikinci bir örnek
ve ayrıca olur
yumurtlayan olmak,
ölçekleri var, zehirli
soğuk kanlı, bacaksız.
İşte benim modelim değil mi?
Mükemmel makul
modelini tasarlayıp tasarlamadığımı
veya bir makine öğrenmesi
algoritma olur

English: 
do it says, if all of these are
true, label it as a reptile.
OK?
And now I give you
a boa constrictor.
Ah.
It's a reptile.
But it doesn't fit the model.
And in particular,
it's not egg-laying,
and it's not poisonous.
So I've got to refine the model.
Or the algorithm has
got to refine the model.
And this, I want to remind you,
is looking at the features.
So I started out
with five features.
This doesn't fit.
So probably what I
should do is reduce it.
I'm going to look at scales.
I'm going to look
at cold blooded.
I'm going to look at legs.
That captures all
three examples.
Again, if you think about
this in terms of clustering,
all three of them
would fit with that.
OK.
Now I give you another example--
chicken.
I don't think it's a reptile.
In fact, I'm pretty
sure it's not a reptile.
And it nicely still
fits this model, right?
Because, while it has scales,
which you may or not realize,
it's not cold blooded,
and it has legs.

Turkish: 
Bunların hepsi varsa, diyor mu?
true, bir sürüngen olarak etiketleyin.
TAMAM?
Ve şimdi sana veriyorum
bir boa yılanı.
Ah.
Bu bir sürüngen.
Fakat bu modele uymuyor.
Ve özellikle,
Yumurtlama değil,
ve zehirli değil.
Bu yüzden modeli düzeltmeliyim.
Veya algoritması var
modeli düzeltmeliyim.
Ve bunu sana hatırlatmak istiyorum.
özelliklere bakıyor.
Bu yüzden başladım
Beş özellikli.
Bu uygun değil.
Muhtemelen ne ben
yapmalı, azaltmak.
Ben ölçeklere bakacağım.
Bakacağım
Soğuk kanlı
Bacaklara bakacağım.
Bu hepsini yakalar
üç örnek.
Tekrar düşünürsen
kümeleme açısından bu,
üçü de
buna uygun olurdu.
TAMAM.
Şimdi sana başka bir örnek vereyim.
tavuk.
Bunun bir sürüngen olduğunu sanmıyorum.
Aslında ben güzelim
Elbette sürüngen değil.
Ve güzel hala
Bu modele uyar, değil mi?
Çünkü terazileri varken,
farkına varabilirsin
soğuk kanlı değil
ve bacakları var.

English: 
So it is a negative example
that reinforces the model.
Sounds good.
And now I'll give
you an alligator.
It's a reptile.
And oh fudge, right?
It doesn't satisfy the model.
Because while it does have
scales and it is cold blooded,
it has legs.
I'm almost done
with the example.
But you see the point.
Again, I've got to think
about how do I refine this.
And I could by
saying, all right.
Let's make it a little more
complicated-- has scales,
cold blooded, 0 or four legs--
I'm going to say it's a reptile.
I'll give you the dart frog.
Not a reptile,
it's an amphibian.
And that's nice because
it still satisfies this.
So it's an example outside
of the cluster that
says no scales,
not cold blooded,
but happens to have four legs.
It's not a reptile.
That's good.
And then I give you--
I have to give you
a python, right?
I mean, there has to
be a python in here.
Oh come on.

Turkish: 
Bu yüzden olumsuz bir örnek
Bu modeli güçlendirir.
Kulağa iyi geliyor.
Ve şimdi vereceğim
sen bir timsahsın
Bu bir sürüngen.
Ve oh şekerleme, değil mi?
Modeli tatmin etmiyor.
Çünkü sahip olduğu sürece
ölçekler ve soğuk kanlıdır
bacakları var.
İşim bitmek üzere
örnek ile.
Ama sen noktayı görüyorsun.
Tekrar düşünmeliyim
Bunu nasıl daraltacağım hakkında.
Ve yapabilirdim
tamam diyerek.
Biraz daha yapalım
karmaşık, ölçekleri var.
soğuk kanlı, 0 veya dört bacaklı--
Bunun bir sürüngen olduğunu söyleyeceğim.
Sana dart kurbağayı vereceğim.
Sürüngen değil
bu bir amfibi.
Ve bu güzel çünkü
hala bunu tatmin ediyor.
Yani bu dışarıda bir örnek
kümenin
Ölçek yok diyor
soğuk kanlı değil
ama dört bacağı olur.
Sürüngen değil.
Bu iyi.
Ve sonra sana--
Sana vermek zorundayım
bir piton, değil mi?
Yani, zorunda olmalı
Burada bir piton ol.
Ah, hadi ama.

English: 
At least grown at
me when I say that.
There has to be a python here.
And I give you
that and a salmon.
And now I am in trouble.
Because look at scales, look
at cold blooded, look at legs.
I can't separate them.
On those features,
there's no way
to come up with a way
that will correctly
say that the python is a
reptile and the salmon is not.
And so there's no easy
way to add in that rule.
And probably my best
thing is to simply go back
to just two features,
scales and cold blooded.
And basically say,
if something has
scales and it's cold blooded,
I'm going to call it a reptile.
If it doesn't have
both of those,
I'm going to say
it's not a reptile.
It won't be perfect.
It's going to incorrectly
label the salmon.
But I've made a design
choice here that's important.
And the design choice is that
I will have no false negatives.
What that means is
there's not going
to be any instance of something
that's not a reptile that I'm

Turkish: 
En azından büyüdü
bunu söylediğimde ben.
Burada bir piton olmalı.
Ve sana veriyorum
Bu ve bir somon.
Ve şimdi başım belada.
Çünkü ölçeklere bak, bak
soğuk kanlı bacaklara bak.
Onları ayıramıyorum.
Bu özelliklerde
hayatta olmaz
bir yolla gelmek
bu doğru olacak
pitonun bir olduğunu söyle
sürüngen ve somon değil.
Ve böylece kolay değil
bu kurala eklemenin bir yolu.
Ve muhtemelen elimden gelenin en iyisini
şey basitçe geri dönmek
sadece iki özelliğe,
ölçekler ve soğuk kanlı.
Ve temelde
eğer bir şey varsa
ölçekler ve soğuk kanlı
Ben buna sürüngen diyeceğim.
Eğer yoksa
ikisi de
Söyleyeceğim
bu bir sürüngen değil.
Mükemmel olmayacak.
Yanlış gidiyor
somonu etiketleyin.
Ama ben bir tasarım yaptım
seçim burada önemli.
Ve tasarım seçimi şudur
Yanlış negatifler almayacağım.
Bunun anlamı
gitmiyor
herhangi bir şeyin örneği olmak
bu benim sürüngen değilim

English: 
going to call a reptile.
I may have some false positives.
So I did that the wrong way.
A false negative
says, everything
that's not a reptile I'm going
to categorize that direction.
I may have some false
positives, in that,
I may have a few things
that I will incorrectly
label as a reptile.
And in particular,
salmon is going
to be an instance of that.
This trade off of false
positives and false negatives
is something that we worry
about, as we think about it.
Because there's no perfect
way, in many cases,
to separate out the data.
And if you think back to my
example of the New England
Patriots, that running back
and that wide receiver were
so close together in
height and weight,
there was no way I'm going to
be able to separate them apart.
And I just have to
be willing to decide
how many false positives
or false negatives
do I want to tolerate.
Once I've figured out what
features to use, which is good,
then I have to decide
about distance.
How do I compare
two feature vectors?
I'm going to say vector
because there could
be multiple dimensions to it.
How do I decide how
to compare them?

Turkish: 
bir sürüngen arayacak.
Bazı yanlış pozitifler olabilir.
Bu yüzden yanlış yoldan yaptım.
Yanlış bir negatif
her şeyi söylüyor
bu sürüngen değilim, gidiyorum
bu yönü kategorize etmek.
Bazı yanlış olabilir
Bu pozitif,
Birkaç şey olabilir
yanlış yapacağım
bir sürüngen olarak etiketleyin.
Ve özellikle,
somon gidiyor
Bunun bir örneği olmak için.
Bu sahte takas
pozitifler ve yanlış negatifler
is something that we worry
about, as we think about it.
Because there's no perfect
way, in many cases,
to separate out the data.
And if you think back to my
example of the New England
Patriots, that running back
and that wide receiver were
so close together in
height and weight,
there was no way I'm going to
be able to separate them apart.
And I just have to
be willing to decide
how many false positives
or false negatives
do I want to tolerate.
Once I've figured out what
features to use, which is good,
then I have to decide
about distance.
How do I compare
two feature vectors?
I'm going to say vector
because there could
be multiple dimensions to it.
How do I decide how
to compare them?

English: 
Because I want to use the
distances to figure out either
how to group things together
or how to find a dividing line
that separates things apart.
So one of the things I have
to decide is which features.
I also have to
decide the distance.
And finally, I
may want to decide
how to weigh relative importance
of different dimensions
in the feature vector.
Some may be more valuable than
others in making that decision.
And I want to show you
an example of that.
So let's go back to my animals.
I started off with a
feature vector that actually
had five dimensions to it.
It was egg-laying, cold
blooded, has scales,
I forget what the other one
was, and number of legs.
So one of the ways I
could think about this
is saying I've got four binary
features and one integer
feature associated
with each animal.
And one way to learn to separate
out reptiles from non reptiles
is to measure the distance
between pairs of examples
and use that distance to
decide what's near each other
and what's not.

Turkish: 
Because I want to use the
distances to figure out either
how to group things together
or how to find a dividing line
that separates things apart.
So one of the things I have
to decide is which features.
I also have to
decide the distance.
And finally, I
may want to decide
how to weigh relative importance
of different dimensions
in the feature vector.
Some may be more valuable than
others in making that decision.
And I want to show you
an example of that.
So let's go back to my animals.
I started off with a
feature vector that actually
had five dimensions to it.
It was egg-laying, cold
blooded, has scales,
I forget what the other one
was, and number of legs.
So one of the ways I
could think about this
is saying I've got four binary
features and one integer
feature associated
with each animal.
And one way to learn to separate
out reptiles from non reptiles
is to measure the distance
between pairs of examples
and use that distance to
decide what's near each other
and what's not.

English: 
And as we've said
before, it will either
be used to cluster things or to
find a classifier surface that
separates them.
So here's a simple way to do it.
For each of these examples,
I'm going to just let true
be 1, false be 0.
So the first four
are either 0s or 1s.
And the last one is
the number of legs.
And now I could say, all right.
How do I measure
distances between animals
or anything else, but these
kinds of feature vectors?
Here, we're going
to use something
called the Minkowski Metric
or the Minkowski difference.
Given two vectors
and a power, p,
we basically take
the absolute value
of the difference between
each of the components
of the vector, raise it to
the p-th power, take the sum,
and take the p-th route of that.
So let's do the two
obvious examples.
If p is equal to 1, I just
measure the absolute distance
between each component, add
them up, and that's my distance.
It's called the
Manhattan metric.

Turkish: 
And as we've said
before, it will either
be used to cluster things or to
find a classifier surface that
separates them.
So here's a simple way to do it.
For each of these examples,
I'm going to just let true
be 1, false be 0.
So the first four
are either 0s or 1s.
And the last one is
the number of legs.
And now I could say, all right.
How do I measure
distances between animals
or anything else, but these
kinds of feature vectors?
Here, we're going
to use something
called the Minkowski Metric
or the Minkowski difference.
Given two vectors
and a power, p,
we basically take
the absolute value
of the difference between
each of the components
of the vector, raise it to
the p-th power, take the sum,
and take the p-th route of that.
So let's do the two
obvious examples.
If p is equal to 1, I just
measure the absolute distance
between each component, add
them up, and that's my distance.
It's called the
Manhattan metric.

Turkish: 
The one you've seen more,
the one we saw last time,
if p is equal to 2, this is
Euclidean distance, right?
It's the sum of the
squares of the differences
of the components.
Take the square root.
Take the square root
because it makes
it have certain
properties of a distance.
That's the Euclidean distance.
So now if I want to measure
difference between these two,
here's the question.
Is this circle closer to the
star or closer to the cross?
Unfortunately, I put
the answer up here.
But it differs, depending
on the metric I use.
Right?
Euclidean distance, well,
that's square root of 2 times 2,
so it's about 2.8.
And that's three.
So in terms of just standard
distance in the plane,
we would say that these two
are closer than those two are.
Manhattan distance,
why is it called that?
Because you can only walk along
the avenues and the streets.
Manhattan distance
would basically
say this is one, two,
three, four units away.
This is one, two,
three units away.

English: 
The one you've seen more,
the one we saw last time,
if p is equal to 2, this is
Euclidean distance, right?
It's the sum of the
squares of the differences
of the components.
Take the square root.
Take the square root
because it makes
it have certain
properties of a distance.
That's the Euclidean distance.
So now if I want to measure
difference between these two,
here's the question.
Is this circle closer to the
star or closer to the cross?
Unfortunately, I put
the answer up here.
But it differs, depending
on the metric I use.
Right?
Euclidean distance, well,
that's square root of 2 times 2,
so it's about 2.8.
And that's three.
So in terms of just standard
distance in the plane,
we would say that these two
are closer than those two are.
Manhattan distance,
why is it called that?
Because you can only walk along
the avenues and the streets.
Manhattan distance
would basically
say this is one, two,
three, four units away.
This is one, two,
three units away.

Turkish: 
And under Manhattan
distance, this is closer,
this pairing is closer
than that pairing is.
Now you're used to
thinking Euclidean.
We're going to use that.
But this is going
to be important
when we think about how
are we comparing distances
between these different pieces.
So typically, we'll
use Euclidean.
We're going to see Manhattan
actually has some value.
So if I go back to my three
examples-- boy, that's
a gross slide, isn't it?
But there we go--
rattlesnake, boa
constrictor, and dart frog.
There is the representation.
I can ask, what's the
distance between them?
In the handout for today,
we've given you a little piece
of code that would do that.
And if I actually run
through it, I get,
actually, a nice
little result. Here
are the distances between those
vectors using Euclidean metric.
I'm going to come back to them.
But you can see the
two snakes, nicely, are
reasonably close to each other.
Whereas, the dart frog is a
fair distance away from that.
Güzel değil mi?
That's a nice separation
that says there's
a difference between these two.

English: 
And under Manhattan
distance, this is closer,
this pairing is closer
than that pairing is.
Now you're used to
thinking Euclidean.
We're going to use that.
But this is going
to be important
when we think about how
are we comparing distances
between these different pieces.
So typically, we'll
use Euclidean.
We're going to see Manhattan
actually has some value.
So if I go back to my three
examples-- boy, that's
a gross slide, isn't it?
But there we go--
rattlesnake, boa
constrictor, and dart frog.
There is the representation.
I can ask, what's the
distance between them?
In the handout for today,
we've given you a little piece
of code that would do that.
And if I actually run
through it, I get,
actually, a nice
little result. Here
are the distances between those
vectors using Euclidean metric.
I'm going to come back to them.
But you can see the
two snakes, nicely, are
reasonably close to each other.
Whereas, the dart frog is a
fair distance away from that.
Nice, right?
That's a nice separation
that says there's
a difference between these two.

Turkish: 
OK.
Now I throw in the alligator.
Sounds like a Dungeons
& Dragons game.
I throw in the alligator, and I
want to do the same comparison.
And I don't get nearly as nice
a result. Because now it says,
as before, the two snakes
are close to each other.
But it says that the dart
frog and the alligator
are much closer, under
this measurement,
than either of them
is to the other.
And to remind you, right,
the alligator and the two
snakes I would like to be close
to one another and a distance
away from the frog.
Because I'm trying to
classify reptiles versus not.
So what happened here?
Well, this is a place where
the feature engineering
is going to be important.
Because in fact, the alligator
differs from the frog
in three features.
And only in two features from,
say, the boa constrictor.
But one of those features
is the number of legs.
And there, while
on the binary axes,

English: 
OK.
Now I throw in the alligator.
Sounds like a Dungeons
& Dragons game.
I throw in the alligator, and I
want to do the same comparison.
And I don't get nearly as nice
a result. Because now it says,
as before, the two snakes
are close to each other.
But it says that the dart
frog and the alligator
are much closer, under
this measurement,
than either of them
is to the other.
And to remind you, right,
the alligator and the two
snakes I would like to be close
to one another and a distance
away from the frog.
Because I'm trying to
classify reptiles versus not.
So what happened here?
Well, this is a place where
the feature engineering
is going to be important.
Because in fact, the alligator
differs from the frog
in three features.
And only in two features from,
say, the boa constrictor.
But one of those features
is the number of legs.
And there, while
on the binary axes,

English: 
the difference is
between a 0 and 1,
here it can be between 0 and 4.
So that is weighing the distance
a lot more than we would like.
The legs dimension is
too large, if you like.
How would I fix this?
This is actually, I would
argue, a natural place
to use Manhattan distance.
Why should I think
that the difference
in the number of legs or the
number of legs difference
is more important than
whether it has scales or not?
Why should I think that
measuring that distance
Euclidean-wise makes sense?
They are really completely
different measurements.
And in fact, I'm
not going to do it,
but if I ran Manhattan
metric on this,
it would get the alligator
much closer to the snakes,
exactly because it differs only
in two features, not three.
The other way I
could fix it would
be to say I'm letting too
much weight be associated
with the difference
in the number of legs.
So let's just make
it a binary feature.

Turkish: 
the difference is
between a 0 and 1,
here it can be between 0 and 4.
So that is weighing the distance
a lot more than we would like.
The legs dimension is
too large, if you like.
How would I fix this?
This is actually, I would
argue, a natural place
to use Manhattan distance.
Why should I think
that the difference
in the number of legs or the
number of legs difference
is more important than
whether it has scales or not?
Why should I think that
measuring that distance
Euclidean-wise makes sense?
They are really completely
different measurements.
And in fact, I'm
not going to do it,
but if I ran Manhattan
metric on this,
it would get the alligator
much closer to the snakes,
exactly because it differs only
in two features, not three.
The other way I
could fix it would
be to say I'm letting too
much weight be associated
with the difference
in the number of legs.
So let's just make
it a binary feature.

English: 
Either it doesn't have
legs or it does have legs.
Run the same classification.
And now you see the
snakes and the alligator
are all close to each other.
Whereas the dart frog, not
as far away as it was before,
but there's a pretty natural
separation, especially
using that number between them.
What's my point?
Choice of features matters.
Throwing too many
features in may, in fact,
give us some overfitting.
And in particular,
deciding the weights
that I want on those
features has a real impact.
And you, as a designer
or a programmer,
have a lot of influence in how
you think about using those.
So feature engineering
really matters.
How you pick the
features, what you use
is going to be important.
OK.
The last piece of
this then is we're
going to look at some examples
where we give you data, got
features associated with them.
We're going to, in some
cases have them labeled,
in other cases not.
And we know how now to
think about how do we
measure distances between them.

Turkish: 
Either it doesn't have
legs or it does have legs.
Run the same classification.
And now you see the
snakes and the alligator
are all close to each other.
Whereas the dart frog, not
as far away as it was before,
but there's a pretty natural
separation, especially
using that number between them.
What's my point?
Choice of features matters.
Throwing too many
features in may, in fact,
give us some overfitting.
And in particular,
deciding the weights
that I want on those
features has a real impact.
And you, as a designer
or a programmer,
have a lot of influence in how
you think about using those.
So feature engineering
really matters.
How you pick the
features, what you use
is going to be important.
OK.
The last piece of
this then is we're
going to look at some examples
where we give you data, got
features associated with them.
We're going to, in some
cases have them labeled,
in other cases not.
And we know how now to
think about how do we
measure distances between them.

English: 
John.
JOHN GUTTAG: You
probably didn't intend
to say weights of features.
You intended to say
how they're scaled.
ERIC GRIMSON: Sorry.
The scales and not
the-- thank you, John.
No, I did.
I take that back.
I did not mean to say
weights of features.
I meant to say the
scale of the dimension
is going to be important here.
Thank you, for the
amplification and correction.
You're absolutely right.
JOHN GUTTAG: Weights, we
use in a different way,
as we'll see next time.
ERIC GRIMSON: And
we're going to see
next time why we're going to
use weights in different ways.
So rephrase it.
Block that out of your mind.
We're going to talk about
scales and the scale on the axes
as being important here.
And we already said
we're going to look
at two different
kinds of learning,
labeled and unlabeled,
clustering and classifying.
And I want to just
finish up by showing you
two examples of that.
How we would think about
them algorithmically,
and we'll look at them
in more detail next time.
As we look at it,
I want to remind
you the things that are
going to be important to you.
How do I measure distance
between examples?
What's the right
way to design that?
What is the right set of
features to use in that vector?

Turkish: 
John.
JOHN GUTTAG: You
probably didn't intend
to say weights of features.
You intended to say
how they're scaled.
ERIC GRIMSON: Sorry.
The scales and not
the-- thank you, John.
No, I did.
I take that back.
I did not mean to say
weights of features.
I meant to say the
scale of the dimension
is going to be important here.
Thank you, for the
amplification and correction.
You're absolutely right.
JOHN GUTTAG: Weights, we
use in a different way,
as we'll see next time.
ERIC GRIMSON: And
we're going to see
next time why we're going to
use weights in different ways.
So rephrase it.
Block that out of your mind.
We're going to talk about
scales and the scale on the axes
as being important here.
And we already said
we're going to look
at two different
kinds of learning,
labeled and unlabeled,
clustering and classifying.
And I want to just
finish up by showing you
two examples of that.
How we would think about
them algorithmically,
and we'll look at them
in more detail next time.
As we look at it,
I want to remind
you the things that are
going to be important to you.
How do I measure distance
between examples?
What's the right
way to design that?
What is the right set of
features to use in that vector?

English: 
And then, what constraints do
I want to put on the model?
In the case of
unlabelled data, how
do I decide how many
clusters I want to have?
Because I can give you a really
easy way to do clustering.
If I give you 100 examples,
I say build 100 clusters.
Every example is
its own cluster.
Distance is really good.
It's really close to itself,
but it does a lousy job
of labeling things on it.
So I have to think
about, how do I
decide how many clusters,
what's the complexity
of that separating service?
How do I basically avoid
the overfitting problem,
which I don't want to have?
So just to remind
you, we've already
seen a little version of
this, the clustering method.
This is a standard way to
do it, simply repeating what
we had on an earlier slide.
If I want to cluster
it into groups,
I start by saying how many
clusters am I looking for?
Pick an example I take as
my early representation.
For every other example
in the training data,
put it to the closest cluster.
Once I've got those, find the
median, repeat the process.

Turkish: 
And then, what constraints do
I want to put on the model?
In the case of
unlabelled data, how
do I decide how many
clusters I want to have?
Because I can give you a really
easy way to do clustering.
If I give you 100 examples,
I say build 100 clusters.
Every example is
its own cluster.
Distance is really good.
It's really close to itself,
but it does a lousy job
of labeling things on it.
So I have to think
about, how do I
decide how many clusters,
what's the complexity
of that separating service?
How do I basically avoid
the overfitting problem,
which I don't want to have?
So just to remind
you, we've already
seen a little version of
this, the clustering method.
This is a standard way to
do it, simply repeating what
we had on an earlier slide.
If I want to cluster
it into groups,
I start by saying how many
clusters am I looking for?
Pick an example I take as
my early representation.
For every other example
in the training data,
put it to the closest cluster.
Once I've got those, find the
median, repeat the process.

English: 
And that led to that separation.
Now once I've got it,
I like to validate it.
And in fact, I should
have said this better.
Those two clusters came without
looking at the two black dots.
Once I put the
black dots in, I'd
like to validate, how well
does this really work?
And that example there is
really not very encouraging.
It's too close.
So that's a natural place to
say, OK, what if I did this
with three clusters?
That's what I get.
I like the that.
All right?
That has a really
nice cluster up here.
The fact that the algorithm
didn't know the labeling
is irrelevant.
There's a nice grouping of five.
There's a nice grouping of four.
And there's a nice grouping
of three in between.
And in fact, if I looked
at the average distance
between examples in
each of these clusters,
it is much tighter
than in that example.
And so that leads to, then,
the question of should I
look for four clusters?
Question, please.

Turkish: 
And that led to that separation.
Now once I've got it,
I like to validate it.
And in fact, I should
have said this better.
Those two clusters came without
looking at the two black dots.
Once I put the
black dots in, I'd
like to validate, how well
does this really work?
And that example there is
really not very encouraging.
It's too close.
So that's a natural place to
say, OK, what if I did this
with three clusters?
That's what I get.
I like the that.
Tamam?
That has a really
nice cluster up here.
The fact that the algorithm
didn't know the labeling
is irrelevant.
There's a nice grouping of five.
There's a nice grouping of four.
And there's a nice grouping
of three in between.
And in fact, if I looked
at the average distance
between examples in
each of these clusters,
it is much tighter
than in that example.
And so that leads to, then,
the question of should I
look for four clusters?
Question, please.

Turkish: 
AUDIENCE: Is that overlap
between the two clusters
not an issue?
ERIC GRIMSON: Yes.
The question is, is the overlap
between the two clusters
a problem?
Yok hayır.
I just drew it
here so I could let
you see where those pieces are.
But in fact, if you like,
the center is there.
Those three points are
all closer to that center
than they are to that center.
So the fact that they
overlap is a good question.
It's just the way I
happened to draw them.
I should really
draw these, not as
circles, but as some little
bit more convoluted surface.
OK?
Having done three, I could
say should I look for four?
Well, those points down
there, as I've already said,
are an example where
it's going to be
hard to separate them out.
And I don't want to overfit.
Because the only way
to separate those out
is going to be to come up with
a really convoluted cluster,
which I don't like.
Tamam?
Let me finish with showing
you one other example
from the other direction.
Which is, suppose I give
you labeled examples.
So again, the goal
is I've got features
associated with each example.
They're going to have
multiple dimensions on it.
But I also know the label
associated with them.

English: 
AUDIENCE: Is that overlap
between the two clusters
not an issue?
ERIC GRIMSON: Yes.
The question is, is the overlap
between the two clusters
a problem?
No.
I just drew it
here so I could let
you see where those pieces are.
But in fact, if you like,
the center is there.
Those three points are
all closer to that center
than they are to that center.
So the fact that they
overlap is a good question.
It's just the way I
happened to draw them.
I should really
draw these, not as
circles, but as some little
bit more convoluted surface.
OK?
Having done three, I could
say should I look for four?
Well, those points down
there, as I've already said,
are an example where
it's going to be
hard to separate them out.
And I don't want to overfit.
Because the only way
to separate those out
is going to be to come up with
a really convoluted cluster,
which I don't like.
All right?
Let me finish with showing
you one other example
from the other direction.
Which is, suppose I give
you labeled examples.
So again, the goal
is I've got features
associated with each example.
They're going to have
multiple dimensions on it.
But I also know the label
associated with them.

Turkish: 
And I want to learn
what is the best
way to come up with a rule that
will let me take new examples
and assign them to
the right group.
A number of ways to do this.
You can simply say I'm looking
for the simplest surface that
will separate those examples.
In my football case that
were in the plane, what's
the best line that
separates them,
which turns out to be easy.
I might look for a more
complicated surface.
And we're going to see
an example in a second
where maybe it's a
sequence of line segments
that separates them out.
Because there's not just one
line that does the separation.
As before, I want to be careful.
If I make it too
complicated, I may
get a really good separator,
but I overfit to the data.
And you're going
to see next time.
I'm going to just
highlight it here.
There's a third
way, which will lead
to almost the same
kind of result
called k nearest neighbors.
And the idea here is I've
got a set of labeled data.
And what I'm going to do
is, for every new example,
say find the k, say the five
closest labeled examples.
And take a vote.

English: 
And I want to learn
what is the best
way to come up with a rule that
will let me take new examples
and assign them to
the right group.
A number of ways to do this.
You can simply say I'm looking
for the simplest surface that
will separate those examples.
In my football case that
were in the plane, what's
the best line that
separates them,
which turns out to be easy.
I might look for a more
complicated surface.
And we're going to see
an example in a second
where maybe it's a
sequence of line segments
that separates them out.
Because there's not just one
line that does the separation.
As before, I want to be careful.
If I make it too
complicated, I may
get a really good separator,
but I overfit to the data.
And you're going
to see next time.
I'm going to just
highlight it here.
There's a third
way, which will lead
to almost the same
kind of result
called k nearest neighbors.
And the idea here is I've
got a set of labeled data.
And what I'm going to do
is, for every new example,
say find the k, say the five
closest labeled examples.
And take a vote.

English: 
If 3 out of 5 or 4 out of 5
or 5 out of 5 of those labels
are the same, I'm going to
say it's part of that group.
And if I have less
than that, I'm
going to leave it
as unclassified.
And that's a nice way
of actually thinking
about how to learn them.
And let me just finish by
showing you an example.
Now I won't use football
players on this one.
I'll use a different example.
I'm going to give
you some voting data.
I think this is
actually simulated data.
But these are a set of
voters in the United States
with their preference.
They tend to vote Republican.
They tend to vote Democrat.
And the two categories are
their age and how far away
they live from Boston.
Whether those are relevant
or not, I don't know,
but they are just two things I'm
going to use to classify them.
And I'd like to say,
how would I fit a curve
to separate those two classes?
I'm going to keep
half the data to test.
I'm going to use half
the data to train.
So if this is my
training data, I
can say what's the best
line that separates these?

Turkish: 
If 3 out of 5 or 4 out of 5
or 5 out of 5 of those labels
are the same, I'm going to
say it's part of that group.
And if I have less
than that, I'm
going to leave it
as unclassified.
And that's a nice way
of actually thinking
about how to learn them.
And let me just finish by
showing you an example.
Now I won't use football
players on this one.
I'll use a different example.
I'm going to give
you some voting data.
I think this is
actually simulated data.
But these are a set of
voters in the United States
with their preference.
They tend to vote Republican.
They tend to vote Democrat.
And the two categories are
their age and how far away
they live from Boston.
Whether those are relevant
or not, I don't know,
but they are just two things I'm
going to use to classify them.
And I'd like to say,
how would I fit a curve
to separate those two classes?
I'm going to keep
half the data to test.
I'm going to use half
the data to train.
So if this is my
training data, I
can say what's the best
line that separates these?

English: 
I don't know about best,
but here are two examples.
This solid line has the
property that all the Democrats
are on one side.
Everything on the other
side is a Republican,
but there are some Republicans
on this side of the line.
I can't find a line that
completely separates these,
as I did with the
football players.
But there is a decent
line to separate them.
Here's another candidate.
That dash line has the
property that on the right side
you've got-- boy, I don't
think this is deliberate,
John, right-- but
on the right side,
you've got almost
all Republicans.
It seems perfectly appropriate.
One Democrat, but there's a
pretty good separation there.
And on the left side,
you've got a mix of things.
But most of the Democrats are
on the left side of that line.
All right?
The fact that left
and right correlates
with distance from Boston is
completely irrelevant here.
But it has a nice punch to it.
JOHN GUTTAG: Relevant,
but not accidental.
ERIC GRIMSON: But
not accidental.
Thank you.
All right.
So now the question is,
how would I evaluate these?
How do I decide
which one is better?
And I'm simply
going to show you,
very quickly, some examples.

Turkish: 
I don't know about best,
but here are two examples.
This solid line has the
property that all the Democrats
are on one side.
Everything on the other
side is a Republican,
but there are some Republicans
on this side of the line.
I can't find a line that
completely separates these,
as I did with the
football players.
But there is a decent
line to separate them.
Here's another candidate.
That dash line has the
property that on the right side
you've got-- boy, I don't
think this is deliberate,
John, right-- but
on the right side,
you've got almost
all Republicans.
It seems perfectly appropriate.
One Democrat, but there's a
pretty good separation there.
And on the left side,
you've got a mix of things.
But most of the Democrats are
on the left side of that line.
Tamam?
The fact that left
and right correlates
with distance from Boston is
completely irrelevant here.
But it has a nice punch to it.
JOHN GUTTAG: Relevant,
but not accidental.
ERIC GRIMSON: But
not accidental.
Teşekkür ederim.
Tamam.
So now the question is,
how would I evaluate these?
How do I decide
which one is better?
And I'm simply
going to show you,
very quickly, some examples.

Turkish: 
First one is to look at what's
called the confusion matrix.
What does that mean?
It says for this, one of
these classifiers for example,
the solid line.
Here are the predictions,
based on the solid line
of whether they would
be more likely to be
Democrat or Republican.
And here is the actual label.
Same thing for the dashed line.
And that diagonal is
important because those are
the correctly labeled results.
Right?
It correctly, in
the solid line case,
gets all of the correct
labelings of the Democrats.
It gets half of the
Republicans right.
But it has some where
it's actually Republican,
but it labels it as a Democrat.
That, we'd like to
be really large.
And in fact, it leads
to a natural measure
called the accuracy.
Which is, just to
go back to that,
we say that these
are true positives.
Meaning, I labeled it as being
an instance, and it really is.
These are true negatives.
I label it as not being an
instance, and it really isn't.
And then these are
the false positives.

English: 
First one is to look at what's
called the confusion matrix.
What does that mean?
It says for this, one of
these classifiers for example,
the solid line.
Here are the predictions,
based on the solid line
of whether they would
be more likely to be
Democrat or Republican.
And here is the actual label.
Same thing for the dashed line.
And that diagonal is
important because those are
the correctly labeled results.
Right?
It correctly, in
the solid line case,
gets all of the correct
labelings of the Democrats.
It gets half of the
Republicans right.
But it has some where
it's actually Republican,
but it labels it as a Democrat.
That, we'd like to
be really large.
And in fact, it leads
to a natural measure
called the accuracy.
Which is, just to
go back to that,
we say that these
are true positives.
Meaning, I labeled it as being
an instance, and it really is.
These are true negatives.
I label it as not being an
instance, and it really isn't.
And then these are
the false positives.

Turkish: 
I labeled it as being an
instance and it's not,
and these are the
false negatives.
I labeled it as not being
an instance, and it is.
And an easy way to measure it
is to look at the correct labels
over all of the labels.
The true positives and
the true negatives,
the ones I got right.
And in that case, both models
come up with a value of 0.7.
So which one is better?
Well, I should validate that.
And I'm going to
do that in a second
by looking at other data.
We could also ask,
could we find something
with less training error?
This is only getting 70% right.
Not great.
Well, here is a more
complicated model.
And this is where
you start getting
worried about overfitting.
Now what I've done,
is I've come up
with a sequence of lines
that separate them.
So everything above this
line, I'm going to say
is a Republican.
Everything below this line,
I'm going to say is a Democrat.
So I'm avoiding that one.
I'm avoiding that one.
I'm still capturing
many of the same things.
And in this case, I get 12 true
positives, 13 true negatives,

English: 
I labeled it as being an
instance and it's not,
and these are the
false negatives.
I labeled it as not being
an instance, and it is.
And an easy way to measure it
is to look at the correct labels
over all of the labels.
The true positives and
the true negatives,
the ones I got right.
And in that case, both models
come up with a value of 0.7.
So which one is better?
Well, I should validate that.
And I'm going to
do that in a second
by looking at other data.
We could also ask,
could we find something
with less training error?
This is only getting 70% right.
Not great.
Well, here is a more
complicated model.
And this is where
you start getting
worried about overfitting.
Now what I've done,
is I've come up
with a sequence of lines
that separate them.
So everything above this
line, I'm going to say
is a Republican.
Everything below this line,
I'm going to say is a Democrat.
So I'm avoiding that one.
I'm avoiding that one.
I'm still capturing
many of the same things.
And in this case, I get 12 true
positives, 13 true negatives,

Turkish: 
and only 5 false positives.
And that's kind of nice.
You can see the 5.
It's those five red
ones down there.
It's accuracy is 0.833.
And now, if I apply that to the
test data, I get an OK result.
It has an accuracy of about 0.6.
I could use this idea to try
and generalize to say could I
come up with a better model.
And you're going to
see that next time.
There could be other ways
in which I measure this.
And I want to use this
as the last example.
Another good measure we use is
called PPV, Positive Predictive
Value which is how many true
positives do I come up with out
of all the things I
labeled positively.
And in this solid model,
in the dashed line,
I can get values about 0.57.
The complex model on the
training data is better.
And then the testing
data is even stronger.
And finally, two other
examples are called
sensitivity and specificity.

English: 
and only 5 false positives.
And that's kind of nice.
You can see the 5.
It's those five red
ones down there.
It's accuracy is 0.833.
And now, if I apply that to the
test data, I get an OK result.
It has an accuracy of about 0.6.
I could use this idea to try
and generalize to say could I
come up with a better model.
And you're going to
see that next time.
There could be other ways
in which I measure this.
And I want to use this
as the last example.
Another good measure we use is
called PPV, Positive Predictive
Value which is how many true
positives do I come up with out
of all the things I
labeled positively.
And in this solid model,
in the dashed line,
I can get values about 0.57.
The complex model on the
training data is better.
And then the testing
data is even stronger.
And finally, two other
examples are called
sensitivity and specificity.

Turkish: 
Sensitivity basically
tells you what percentage
did I correctly find.
And specificity
said what percentage
did I correctly reject.
And I show you this
because this is
where the trade-off comes in.
If sensitivity is how
many did I correctly
label out of those
that I both correctly
labeled and incorrectly
labeled as being negative,
how many them did
I correctly label
as being the kind that I want?
I can make sensitivity 1.
Label everything is the
thing I'm looking for.
Great.
Everything is correct.
But the specificity will be 0.
Because I'll have a bunch of
things incorrectly labeled.
I could make the specificity
1, reject everything.
Say nothing as an instance.
True negatives goes to 1, and
I'm in a great place there,
but my sensitivity goes to 0.
I've got a trade-off.
As I think about the machine
learning algorithm I'm using

English: 
Sensitivity basically
tells you what percentage
did I correctly find.
And specificity
said what percentage
did I correctly reject.
And I show you this
because this is
where the trade-off comes in.
If sensitivity is how
many did I correctly
label out of those
that I both correctly
labeled and incorrectly
labeled as being negative,
how many them did
I correctly label
as being the kind that I want?
I can make sensitivity 1.
Label everything is the
thing I'm looking for.
Great.
Everything is correct.
But the specificity will be 0.
Because I'll have a bunch of
things incorrectly labeled.
I could make the specificity
1, reject everything.
Say nothing as an instance.
True negatives goes to 1, and
I'm in a great place there,
but my sensitivity goes to 0.
I've got a trade-off.
As I think about the machine
learning algorithm I'm using

English: 
and my choice of
that classifier,
I'm going to see
a trade off where
I can increase specificity at
the cost of sensitivity or vice
versa.
And you'll see a nice technique
called ROC or Receiver Operator
Curve that gives you a sense of
how you want to deal with that.
And with that, we'll
see you next time.
We'll take your
question off line
if you don't mind, because
I've run over time.
But we'll see you next
time where Professor Guttag
will show you examples of this.

Turkish: 
and my choice of
that classifier,
I'm going to see
a trade off where
I can increase specificity at
the cost of sensitivity or vice
versa.
And you'll see a nice technique
called ROC or Receiver Operator
Curve that gives you a sense of
how you want to deal with that.
And with that, we'll
see you next time.
We'll take your
question off line
if you don't mind, because
I've run over time.
But we'll see you next
time where Professor Guttag
will show you examples of this.
