【光能蜗牛的人工智能之旅】week1 : 3.Welcome

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.
欢迎来到这个免费的在线机器学习课程。机器学习是最近最令人兴奋的技术之一。在这门课上，你可以学习到最先进的算法，也可以练习自己实现和部署这些算法。

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.
你可能一天会不知不觉地使用一个学习算法几十次。每次你使用像谷歌或必应这样的网络搜索引擎来搜索互联网时，之所以能取得如此好的效果，其中一个原因就是由谷歌或微软实现的学习算法已经学会了如何对网页进行排名。每次你使用Facebook或苹果的照片输入应用程序，它都能识别你朋友的照片，这也是机器学习。

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.
每次你阅读你的电子邮件和你的垃圾邮件过滤器将你从大量的垃圾邮件中抽离出来，这也是一个学习算法。对我来说，我兴奋的原因之一是人工智能梦想有一天能制造出像你我一样智能的机器。我们离这一目标还有很长一段路要走，但许多人工智能研究人员认为，实现这一目标的最佳途径是通过学习算法，试图模仿人类大脑的学习方式。

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.
我也会这门课上讲一些这方面的内容。在这门课上，你将学习最先进的机器学习算法。事实证明，如果你不知道如何让这些东西来解决你关心的问题，这只会让你感觉算法和数学并不是那么好。

So, we've also spent a lot of time developing exercises for you to implement each of these algorithms and see how they work for 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.
我们也花了很多时间为你们做练习来实现这些算法看看它们是如何工作的。那么，为什么机器学习在今天如此流行呢?事实证明，机器学习是从人工智能领域发展起来的一个领域。

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.
我们想构建智能机器,事实证明,有一些基本的东西,我们可以编写一个机器让求类似如"找到从a到b的最短路径",但在大多数情况下,只是不知道如何编写AI程序让机器做更有趣的事情,如网络搜索或照片标签或电子邮件反垃圾邮件。人们意识到，做这些事情的唯一方法就是让机器学会自己做。

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.
因此，机器学习作为计算机的一种新功能被开发出来，今天它涉及到工业和基础科学的许多领域。对我来说,我从事机器学习的工作,我每周基本可能在跟直升机飞行员,生物学家,一群计算机系统的人交谈中(我的同事在斯坦福大学)，以及每周平均两到三次收到从行业从硅谷的人联系我有兴趣学习算法应用到自己的问题的电子邮件的频率中结束。这是机器学习涉及到这些问题的一个迹象。

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.
在硅谷有自主机器人学，计算生物学，以及很多机器学习正在产生影响的东西。这里还有一些机器学习的例子。如数据库挖掘。

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.
机器学习如此普及的原因之一是网络的发展和自动化的发展，这意味着我们拥有比以往任何时候都要大得多的数据集。举个例子，现在很多硅谷公司都在收集网络点击数据，也叫点击流数据，他们试图用机器学习算法来挖掘这些数据来更好地理解用户，更好地为用户服务，这是硅谷现在的一个很大的部分。

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.
医疗记录。
随着自动化的到来，我们现在有了电子病历，所以如果我们能把病历变成医学知识，那么我们就能更好地了解疾病。计算生物学。随着自动化的再次出现，生物学家正在收集大量关于基因序列、DNA序列等的数据，而运行算法的机器正在让我们更好地理解人类基因组，以及它对人类意味着什么。

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.
在工程领域也是如此，在所有的工程领域，我们有越来越大的数据集，我们试图用学习算法来理解这些数据集。第二种机械应用是我们无法手工编程的。举个例子，我在自动直升机上工作了很多年.

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]
我们只是不知道如何编写一个计算机程序使这架直升机自己飞行。唯一起作用的是让电脑自己学习如何驾驶这架直升机。[直升机盘旋] (video演示)

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.
手写识别
如今，将一封邮件发送到世界各地，无论是美国还是其他国家，都非常便宜 ,原因之一是当你写这样的一个信封(video演示), 有一个学习算法,学习了如何阅读你的笔迹,以便它可以用它的方式自动路由这个信封 ,所以我们只需花费几美分成本就可以将这个东西送出数千英里。事实上，如果你曾经了解过自然语言处理或计算机视觉的领域，这些都是人工智能理解语言或图像相关的领域。当今大多数自然语言处理和计算机视觉都应用了机器学习。

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.
机器学习算法也在用户自定制化程序(self-customizing program)中有着广泛的应用,
每当你使用亚马逊 Netflix或iTunes Genius的服务时 都会收到它们为你量身推荐的电影或产品 这就是通过学习算法来实现的 可以相信 这些应用都有着上千万的用户 而针对这些海量的用户 编写千万个不同的程序显然是不可能的

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.
唯一有效的解决方案就是开发出能够自我学习 定制出符合你喜好的并据此进行推荐的软件 最后 机器学习算法已经被应用于探究 人类的学习方式 并试图理解人类的大脑 我们也将会了解到研究者是如何运用机器学习的工具 来一步步实现伟大的人工智能的梦想

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.
就在几个月前我的一位学生给我看了一篇文章 文中列举了当今12个最主要的IT技能 这些技术可以让信息技术行业的招聘官无法拒绝你 虽然这是一篇略显老旧的文章 但所有技能中最重要的便是机器学习

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.
在斯坦福 向我咨询有没有即将毕业的研究机器学习学生的雇主 远远多于我们这儿每年毕业的机器学习的学生 因而我觉得对机器学习这一技能的需求仍有着巨大的缺口 而现在正是学习它的绝佳机会 我希望你们能在这门课中收获良多 在接下来的视频中 我将更正式地定义什么是机器学习

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.
也会讨论机器学习主要面对的几类问题和相关算法 你也会学习一些主要的机器学习术语 并对不同的算法和其适用的场景 有初步的了解