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Collaborative Filtering Algorithm

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Aprendizagem Automática

Universidade de Stanford

4.9 (105,106 classificações) | 2.3M alunos inscritos

Visualizar o programa do curso

A Aprendizagem Automática é a ciência que faz com que os computadores exerçam seu papel de forma natural sem que pareçam explicitamente programados para tal. Na última década, a aprendizagem automática foi responsável pelo surgimento dos carros automáticos, por recursos de reconhecimento de fala, otimizou as buscas na web e possibilitou um avanço enorme na compreensão do genoma humano. Atualmente a aprendizagem automática está tão difundida, que você provavelmente a utiliza várias vezes ao dia sem que perceba. Muitos pesquisadores também acreditam que esse é o caminho para levar a inteligência artificial a níveis equivalentes aos humanos. Neste curso, você vai conhecer as técnicas mais eficazes de aprendizagem automática, colocá-las em prática e ver como adequar essas técnicas para suas necessidades. O mais importante é que você não aprenderá apenas na teoria e sim terá o conhecimento prático necessário para aplicar as técnicas de forma rápida e exitosa e solucionar os problemas em seus projetos. Além disso, o aluno conhecerá alguns dos melhores e mais inovadores métodos utilizados no Vale do Silício no que se refere à aprendizagem automática e IA. Este curso traz uma introdução abrangente sobre aprendizagem automática, mineração de dados, e reconhecimento de padrões. Os tópicos estudados incluem: (i) Aprendizagem supervisionada (algoritmos paramétricos e não paramétricos, máquinas vetoriais de suporte, núcleos e redes neurais). (ii) Aprendizagem não supervisionada (Algoritmos de agrupamento - clustering, redução dimensional, sistemas de recomendação, a algoritmos hierárquicos). (iii) A melhores técnicas de aprendizagem automática (viés indutivo / variância; processo de inovação em aprendizagem automática e inteligência artificial). O curso abordará também inúmeros estudos de caso e aplicações para que o aluno possa aprender como aplicar os algoritmos na construção de robôs inteligentes (percepção, controle), reconhecimento de texto (para pesquisa na web, anti-spam), visão computacional, informática médica, áudio, mineração de dados, entre muitas outras áreas de aplicação.</p>

Habilidades que você aprenderá

Logistic Regression, Artificial Neural Network, Machine Learning (ML) Algorithms, Machine Learning

Avaliações

4.9 (105,106 classificações)

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MN

Jun 15, 2016

Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.

PT

Sep 01, 2018

Sub title should be corrected. Since I'm not that good in English but I know when there're mis-traslated or wrong sub title. If you fix this problems , I thin it helps many students a lot. Thanks!!!!!

Na lição

Recommender Systems

When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.

Collaborative Filtering10:14

Collaborative Filtering Algorithm8:26

Ministrado por

Andrew Ng
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain

In the last couple videos, we

talked about the ideas of

how, first, if you're

given features for movies, you

can use that to learn parameters data for users.

And second, if you're given parameters for the users,

you can use that to learn features for the movies.

In this video we're going

to take those ideas and put

them together to come up

with a collaborative filtering algorithm.

So one of the things we worked

out earlier is that if

you have features for the

movies then you can solve

this minimization problem to find

the parameters theta for your users.

And then we also

worked that out, if you

are given the parameters theta,

you can also use that to

estimate the features x, and

you can do that by solving this minimization problem.

So one thing you

could do is actually go back and forth.

Maybe randomly initialize the parameters

and then solve for theta,

solve for x, solve for theta,

solve for x. But, it

turns out that there is a

more efficient algorithm that doesn't

need to go back and forth

between the x's and the

thetas, but that can solve

for theta and x simultaneously.

And here it is. What we are going to do, is basically take

both of these optimization objectives, and

put them into the same objective.

So I'm going to define the

new optimization objective j, which

is a cost function, that

is a function of my features

x and a function

of my parameters theta.

And, it's basically the two optimization objectives

I had on top, but I put together.

So, in order to

explain this, first, I want to point out that this

term over here, this squared

error term, is the same

as this squared error term and the

summations look a little bit

different, but let's see what the summations are really doing.

The first summation is sum

over all users J and

then sum over all movies rated by that user.

So, this is really summing over all

pairs IJ, that correspond

to a movie that was rated by a user.

Sum over J says, for every

user, the sum of

all the movies rated by that user.

This summation down here, just does things in the opposite order.

This says for every movie

I, sum over all

the users J that have

rated that movie and so, you

know these summations, both of these

are just summations over all pairs

ij for which

r of i J is equal to 1.

It's just something over all the

user movie pairs for which you have a rating.

and so those two terms

up there is just

exactly this first term, and

I've just written the summation here explicitly,

where I'm just saying the sum

of all pairs IJ, such that

RIJ is equal to 1.

So what we're going

to do is define a

combined optimization objective that

we want to minimize in order

to solve simultaneously for x and theta.

And then the other terms in

the optimization objective are this,

which is a regularization in terms of theta.

So that came down here and

the final piece is this

term which is

my optimization objective for

the x's and that became this.

And this optimization objective

j actually has an interesting property

that if you were to hold

the x's constant and just

minimize with respect to the thetas then

you'd be solving exactly this problem,

whereas if you were to do

the opposite, if you were to

hold the thetas constant, and minimize

j only with respect to

the x's, then it becomes equivalent to this.

Because either this term

or this term is constant if

you're minimizing only the respective x's or only respective thetas.

So here's an optimization

objective that puts together my

cost functions in terms of x and in terms of theta.

And in order to

come up with just one

optimization problem, what we're going

to do, is treat this

cost function, as a

function of my features

x and of my user

pro user parameters data and

just minimize this whole thing, as

a function of both the

Xs and a function of the thetas.

And really the only difference

between this and the older

algorithm is that, instead

of going back and forth, previously

we talked about minimizing with respect

to theta then minimizing with respect to x,

whereas minimizing with respect to theta,

minimizing with respect to x and so on.

In this new version instead of

sequentially going between the

2 sets of parameters x and theta,

what we are going to do

is just minimize with respect

to both sets of parameters simultaneously.

Finally one last detail

is that when we're learning the features this way.

Previously we have been using

this convention that

we have a feature x0 equals

one that corresponds to an interceptor.

When we are using this

sort of formalism where we're are actually learning the features,

we are actually going to do away with this convention.

And so the features we are going to learn x, will be in Rn.

Whereas previously we had

features x and Rn + 1 including the intercept term.

By getting rid of x0 we now just have x in Rn.

And so similarly, because the

parameters theta is in

the same dimension, we now

also have theta in RN

because if there's no

x0, then there's no need

parameter theta 0 as well.

And the reason we do away

with this convention is because

we're now learning all the features, right?

So there is no need

to hard code the feature that is always equal to one.

Because if the algorithm really wants

a feature that is always equal

to 1, it can choose to learn one for itself.

So if the algorithm chooses,

it can set the feature X1 equals 1.

So there's no need

to hard code the feature of

001, the algorithm now has

the flexibility to just learn it by itself. So, putting

everything together, here is

our collaborative filtering algorithm.

first we are going to

initialize x and

theta to small random values.

And this is a little bit

like neural network training, where there

we were also initializing all the parameters of a neural network to small random values.

Next we're then going

to minimize the cost function using

great intercepts or one of the advance optimization algorithms.

So, if you take derivatives you

find that the great intercept

like these and so this

term here is the

partial derivative of the cost function,

I'm not going to write that out,

with respect to the feature

value Xik and similarly

this term here is also

a partial derivative value of

the cost function with respect to the parameter

theta that we're minimizing.

And just as a reminder, in

this formula that we no

longer have this X0 equals

1 and so we have

that x is in Rn and theta is a Rn.

In this new formalism, we're regularizing

every one of our perimeters theta, you know, every one of our parameters Xn.

There's no longer the special

case theta zero, which was

regularized differently, or which

was not regularized compared to

the parameters theta 1 down to theta.

So there is now no longer a

theta 0, which is why

in these updates, I did not

break out a special case for k equals 0.

So we then use gradient descent

to minimize the cost function

j with respect to the

features x and with respect to the parameters theta.

And finally, given a

user, if a user

has some parameters, theta, and

if there's a movie with

some sort of learned features x,

we would then predict that that

movie would be given a

star rating by that user

of theta transpose j. Or

just to fill those in,

then we're saying that if user

J has not yet

rated movie I, then

what we do is predict that

user J is going to

rate movie I according to

theta J transpose Xi.

So that's the collaborative

filtering algorithm and if

you implement this algorithm you actually get a pretty

decent algorithm that will simultaneously

learn good features for hopefully

all the movies as well as

learn parameters for all the

users and hopefully give

pretty good predictions for how

different users will rate different movies that they have not yet rated

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