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>
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Do curso por Stanford University
Aprendizagem Automática
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Introduction
Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.
Conheça os instrutores
Andrew NgCo-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Welcome to this free online class on machine learning. Machine learning is one
of the most exciting recent technologies. And in this class, you learn about the
state of the art and also gain practice implementing and deploying these algorithms
yourself. You've probably use a learning algorithm dozens of times a day without
knowing it. Every time you use a web search engine like Google or Bing to
search the internet, one of the reasons that works so well is because a learning
algorithm, one implemented by Google or Microsoft, has learned how to rank web
pages. Every time you use Facebook or Apple's photo typing application and it
recognizes your friends' photos, that's also machine learning. Every time you read
your email and your spam filter saves you from having to wade through tons of spam
email, that's also a learning algorithm. For me one of the reasons I'm excited is
the AI dream of someday building machines as intelligent as you or me. We're a long
way away from that goal, but many AI researchers believe that the best way to
towards that goal is through learning algorithms that try to mimic how the human
brain learns. I'll tell you a little bit about that too in this class. In this
class you learn about state-of-the-art machine learning algorithms. But it turns
out just knowing the algorithms and knowing the math isn't that much good if
you don't also know how to actually get this stuff to work on problems that you
care about. So, we've also spent a lot of time developing exercises for you to
implement each of these algorithms and see how they work fot yourself. So why is
machine learning so prevalent today? It turns out that machine learning is a
field that had grown out of the field of AI, or artificial intelligence. We wanted
to build intelligent machines and it turns out that there are a few basic things that
we could program a machine to do such as how to find the shortest path from A to B.
But for the most part we just did not know how to write AI programs to do the more
interesting things such as web search or photo tagging or email anti-spam. There
was a realization that the only way to do these things was to have a machine learn
to do it by itself. So, machine learning was developed as a new capability for
computers and today it touches many segments of industry and basic science.
For me, I work on machine learning and in a typical week I might end up talking to
helicopter pilots, biologists, a bunch of computer systems people (so my
colleagues here at Stanford) and averaging two or three times a week I get email from
people in industry from Silicon Valley contacting me who have an interest in
applying learning algorithms to their own problems. This is a sign of the range of
problems that machine learning touches. There is autonomous robotics, computational
biology, tons of things in Silicon Valley that machine learning is having an impact
on. Here are some other examples of machine learning. There's database mining.
One of the reasons machine learning has so pervaded is the growth of the web and the
growth of automation All this means that we have much larger data sets than ever
before. So, for example tons of Silicon Valley companies are today collecting web
click data, also called clickstream data, and are trying to use machine learning
algorithms to mine this data to understand the users better and to serve the users
better, that's a huge segment of Silicon Valley right now. Medical
records. With the advent of automation, we now have electronic medical records, so if
we can turn medical records into medical knowledge, then we can start to understand
disease better. Computational biology. With automation again, biologists are
collecting lots of data about gene sequences, DNA sequences, and so on, and
machines running algorithms are giving us a much better understanding of the human
genome, and what it means to be human. And in engineering as well, in all fields of
engineering, we have larger and larger, and larger and larger data sets, that
we're trying to understand using learning algorithms. A second range of machinery
applications is ones that we cannot program by hand. So for example, I've
worked on autonomous helicopters for many years. We just did not know how to write a
computer program to make this helicopter fly by itself. The only thing that worked
was having a computer learn by itself how to fly this helicopter. [Helicopter whirling]
Handwriting recognition. It turns out one of the reasons it's so inexpensive today to
route a piece of mail across the countries, in the US and internationally,
is that when you write an envelope like this, it turns out there's a learning
algorithm that has learned how to read your handwriting so that it can automatically
route this envelope on its way, and so it costs us a few cents to send this thing
thousands of miles. And in fact if you've seen the fields of natural language
processing or computer vision, these are the fields of AI pertaining to
understanding language or understanding images. Most of natural language processing
and most of computer vision today is applied machine learning. Learning
algorithms are also widely used for self- customizing programs. Every time you go to
Amazon or Netflix or iTunes Genius, and it recommends the movies or products and
music to you, that's a learning algorithm. If you think about it they have million
users; there is no way to write a million different programs for your million users.
The only way to have software give these customized recommendations is to become
learn by itself to customize itself to your preferences. Finally learning
algorithms are being used today to understand human learning and to
understand the brain. We'll talk about how researches are using this to make
progress towards the big AI dream. A few months ago, a student showed me an article
on the top twelve IT skills. The skills that information technology hiring
managers cannot say no to. It was a slightly older article, but at the top of
this list of the twelve most desirable IT skills was machine learning. Here at
Stanford, the number of recruiters that contact me asking if I know any
graduating machine learning students is far larger than the machine learning
students we graduate each year. So I think there is a vast, unfulfilled demand
for this skill set, and this is a great time to be learning about machine learning, and I
hope to teach you a lot about machine learning in this class. In the next video,
we'll start to give a more formal definition of what is machine learning.
And we'll begin to talk about the main types of machine learning problems and
algorithms. You'll pick up some of the main machine learning terminology, and
start to get a sense of what are the different algorithms, and when each one
might be appropriate.
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