1 - 1 - Welcome (7 min).srt

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Welcome to this free online class on
machine learning. Machine learning is one
欢迎来到机器学习免费在线课程。机器学习是
(字幕整理：中国海洋大学 黄海广,haiguang2000@qq.com )

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of the most exciting recent technologies.
And in this class, you learn about the
目前最激动人心的技术之一。本课程中，你将学习

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state of the art and also gain practice
implementing and deploying these algorithms
机器学习的发展，并且实现这些算法。

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yourself. You've probably use a learning
algorithm dozens of times a day without
你每天都要多次使用学习，但并没有意识到。

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knowing it. Every time you use a web
search engine like Google or Bing to
每次当你使用Google或Bing等搜索引擎时，

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search the internet, one of the reasons
that works so well is because a learning
它能给出如此满意的结果，原因之一就是使用了学习算法。

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algorithm, one implemented by Google or
 Microsoft, has learned how to rank web
由Google或微软实现的算法学会如何给网页排序。

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pages. Every time you use Facebook or
Apple's photo typing application and it
每次你使用Facebook或苹果的相片分类功能，

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recognizes your friends' photos, that's
also machine learning. Every time you read
它能识别出你朋友的相片，这也是机器学习。每次当你阅读邮件时，

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your email and your spam filter saves you
 from having to wade through tons of spam
你的垃圾邮件过滤器帮助你过滤大量的垃圾邮件，

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email, that's also a learning algorithm.
For me one of the reasons I'm excited is
这也是学习算法。对我而言，我兴奋的原因之一是

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the AI dream of someday building machines
as intelligent as you or me. We're a long
AI的梦想就是有一天能建造像你我一样智能的机器。

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way away from that goal, but many AI
researchers believe that the best way to
我们离这个目标还很远，但是许多AI研究者相信实现这个目标的最好方法

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towards that goal is through learning
 algorithms that try to mimic how the human
就是采用学习算法试图模拟人类大脑是如何学习的。

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brain learns. I'll tell you a little bit
 about that too in this class. In this
在本课程中，我将会向你们介绍部分这方面的内容。

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class you learn about state-of-the-art
machine learning algorithms. But it turns
本课程中，你将学习机器学习算法的发展。

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out just knowing the algorithms and
knowing the math isn't that much good if
但是，仅知道算法以及算法的数学含义，

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you don't also know how to actually get
this stuff to work on problems that you
却不知道如何在你关心的问题上运用，是远远不够的。

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care about. So, we've also spent a lot
of time developing exercises for you to
因此，我们也会花很多时间让你练习如何

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implement each of these algorithms and
 see how they work for yourself. So why is
实现每一个算法，如何被你所用。

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machine learning so prevalent today?
It turns out that machine learning is a
这就是为什么机器学习如此流行。机器学习

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field that had grown out of the field of
AI, or artificial intelligence. We wanted
是从AI发展出来的一个领域。我们想

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to build intelligent machines and it turns
out that there are a few basic things that
建造智能机器，那就是说我们要编程使机器能做很多基本的事情，

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we could program a machine to do such as
how to find the shortest path from A to B.
比如找到从A到B的最短路径。

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But for the most part we just did not know
how to write AI programs to do the more
但大多数情况下，我们不知道如何编写AI程序使机器做更多
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interesting things such as web search or
photo tagging or email anti-spam. There
有趣的事情，如网页搜索、相片标记、反垃圾邮件。

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was a realization that the only way to do
 these things was to have a machine learn
人们认识到做到这些事情，唯一的方法就是使机器本事学习如何去做。

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to do it by itself. So, machine learning
was developed as a new capability for
因此，机器学习是计算机需要开发的一项新能力，

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computers and today it touches many
segments of industry and basic science.
并且它设计工业和基础科学中的许多内容。

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For me, I work on machine learning and
in a typical week I might end up talking to
对我而言，我研究机器学习，并且在有代表性的一周中，我可能会与

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helicopter pilots, biologists, a bunch
of computer systems people (so my
直升飞机飞行员，生物学家，很多计算机系统的人员交流

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colleagues here at Stanford) and averaging
 two or three times a week I get email from
并且每周2~3次与

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people in industry from Silicon Valley
contacting me who have an interest in
硅谷工业界的人员互通email，他们对在他们的问题上

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applying learning algorithms to their own
problems. This is a sign of the range of
应用学习算法感兴趣。以下是一些

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problems that machine learning touches.
There is autonomous robotics, computational
机器学习涉及的领域，自主机器人，计算生物学，

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biology, tons of things in Silicon Valley
 that machine learning is having an impact
以及其它一些被机器学习影响的领域。

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on. Here are some other examples of
 machine learning. There's database mining.
还有一些其它的例子，如数据挖掘。

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One of the reasons machine learning has so
pervaded is the growth of the web and the
机器学习如此普遍的原因之一就是网络的快速发展和

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growth of automation. All this means that
we have much larger data sets than ever
自动化技术的快速发展。这意味着我们拥有了前所未有的大量的数据集。

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before. So, for example tons of Silicon
Valley companies are today collecting web
因此，现在大量硅谷公司收集网络点击数据，

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click data, also called clickstream data,
and are trying to use machine learning
被称为点击流数据，并试图采用机器学习算法

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algorithms to mine this data to understand
the users better and to serve the users
来挖掘数据，更好地理解用户，并更好地为用户服务。

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better, that's a huge segment of
Silicon Valley right now. Medical
这占目前硅谷工作的很大部分。

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records. With the advent of automation, we
now have electronic medical records, so if
医疗记录。随着自动化的出现，现在我们使用电子医疗记录，

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we can turn medical records into medical
knowledge, then we can start to understand
因此，假如我们能将医疗记录转化为医疗知识，那我们就能更好地理解疾病。

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disease better. Computational biology.
With automation again, biologists are
计算生物学。还是因为自动化，生物学家

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collecting lots of data about gene
sequences, DNA sequences, and so on, and
收集了大量的数据，关于基因序列，DNA序列等。

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machines learning algorithms are giving us
a much better understanding of the human
机器学习算法让我们更好地理解基因组，

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genome, and what it means to be human.
And in engineering as well, in all fields of
以及它对人类的意义。在工程学中，在工程学所有的领域，

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engineering, we have larger and larger,
and larger and larger data sets, that
我们有越来越大，越来越大的数据集，

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we're trying to understand using learning
algorithms. A second range of machinery
我们正设法采用学习算法来理解。

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applications is ones that we cannot
program by hand. So for example, I've
机器应用的第二个领域是我们无法手动编写程序。例如，

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worked on autonomous helicopters for many
 years. We just did not know how to write a
我们已经在自动直升飞机领域研究了很多年，仍不知道如何编写

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computer program to make this helicopter
fly by itself. The only thing that worked
计算机程序使得直升飞机自己飞行。唯一有用的

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was having a computer learn by itself how
to fly this helicopter. [Helicopter whirling]
就是使计算机自己学习如何使直升飞机飞行。

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Handwriting recognition. It turns out one
of the reasons it's so inexpensive today to
手写体识别。今天，邮寄不再昂贵的原因之一，

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route a piece of mail across the
countries, in the US and internationally,
无论在美国或国际上，

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is that when you write an envelope like
 this, it turns out there's a learning
就是当你写好信封后，有学习算法

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algorithm that has learned how to read your
handwriting so that it can automatically
来学习如何读取你的手写体，使得它能自动地

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route this envelope on its way, and so it
costs us a few cents to send this thing
给你的信规划路线。因此，邮寄几千公里之外的信也只需要花费几分钱。

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thousands of miles. And in fact if you've
seen the fields of natural language
事实上，假如你知道自然语言处理

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processing or computer vision,
these are the fields of AI pertaining to
或计算机视觉，这些都是AI中有关

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understanding language or understanding
images. Most of natural language processing
理解语言或理解图像的领域。今天，大部分的自然语言处理和

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and most of computer vision today is
 applied machine learning. Learning
大部分的计算机视觉采用机器学习。

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algorithms are also widely used for self-
customizing programs. Every time you go to
学习算法还应用在自我定制程序中。每次当你使用

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Amazon or Netflix or iTunes Genius, and it
recommends the movies or products and
亚马逊或Netflix或iTunes天才，它就会推荐电影或产品或音乐给你，

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music to you, that's a learning algorithm.
If you think about it they have million
这就是学习算法。假如你想象一下，他们有百万用户，

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users; there is no way to write a million
different programs for your million users.
不可能为百万用户编写百万个不同的程序。

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The only way to have software give these
customized recommendations is to become
用软件给出这些客户推荐的唯一方法就是

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learn by itself to customize itself to
your preferences. Finally learning
自我学习并为你定制你偏爱的东西。

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algorithms are being used today to
understand human learning and to
另外，今天学习算法还被使用来理解人类的学习

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understand the brain. We'll talk about
how researches are using this to make
和大脑。我们将会讨论研究者是如何使用这些来

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progress towards the big AI dream. A few
months ago, a student showed me an article
朝着AI梦前进的。几个月前，一个学生给我看了篇文章，

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on the top twelve IT skills. The skills
that information technology hiring
列出了12个IT技术。这些技术是信息科技雇主

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managers cannot say no to. It was a
 slightly older article, but at the top of
最喜爱的。这篇文章有点旧了，但是在这张表中，

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this list of the twelve most desirable IT
 skills was machine learning. Here at
机器学习位列第一。

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Stanford, the number of recruiters
that contact me asking if I know any
在斯坦福，联系我的招聘人员，需要的机器学习毕业的学生

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graduating machine learning students
is far larger than the machine learning
的数量远大于每年毕业的机器学习的学生。

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students we graduate each year. So I
think there is a vast, unfulfilled demand
因此，我认为这项技术有大量的未完成的需求。

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for this skill set, and this is a great time to
be learning about machine learning, and I
这也是一个学习机器学习的好时机。

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hope to teach you a lot about machine
learning in this class. In the next video,
我希望能在这门课中教给你机器学习的知识。在下一个视频中，

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we'll start to give a more formal
definition of what is machine learning.
我们将开始给出机器学习的更正式的定义。

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And we'll begin to talk about the main
types of machine learning problems and
我们将开始谈到机器学习问题的主要类型，及算法。

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algorithms. You'll pick up some of the
main machine learning terminology, and
你们将会学到一些主要的机器学习术语，

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start to get a sense of what are the
different algorithms, and when each one
及开始理解不同的机器学习算法，及每个算法在什么时候是合适的。

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might be appropriate.
也许管用。