1-2 Welcome

02-28

Machine learning is one of the most exciting recent technologies.And in this class,you learn about the state of the art(最前沿的) and also gain practice implementing and deploying these algorithms yourself. Youve 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 research 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 Apples photo typing application , and it recognizes your friends photos, thats also machine learning. Every time you read your email and your spam filter saves you from having to wade through tons of spam email, thats also a learning algorithm. For me one of the reasons Im excited is the AI dream of someday building machines as intelligent as you and I. but many AI researchers believe that the best way to towards that goal is through algorithms that try to mimic how the human brain learns. Ill 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 isnt that much good if you dont also know how to actually get this stuff(理論，材料) to work on problems that you care about. So, weve 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. 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(標籤) 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(環節；部分) 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(矽谷) 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(自主機器人), computational biology(計算生物學)， tons of things in Silicon Valley that machine learning is having an impact on.

Here are some other examples of machine learning. Theres database mining(數據挖掘). 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(集合) than ever before. So, for example tons of Silicon Valley companies are today collecrting web click data also called cilckstream data, and are trying to use machine learning algorithms to mine this data to understand the users better and to serve the users better, thats a huge segment of Silicon Valley right now. Medical records. With the advent(出現；到來) of automation, we now have electronic madical 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 collection 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(基因組；染色體) and what it means to be human. And in engineering as well, in all fields of engineering, we have lager and larger, and lager and larger data sets that were trying to understand using learning algorithms. A second range of machinery applications(機器應用) is ones that we cannot program by hand, So for example, Ive 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.Handwriting recognition(手寫識別),It turns out one of the reasons its so inexpensive today to route(次序，分類) a piece of mail across the countries in the US and internationally is that when you write an envelope like this it turns out theres 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 fo miles, And in fact if youve seen the fields of natural language processing(NLP自然語言處理) or computer vision(計算機視覺), 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(用戶自製化程序).Every time you go to Amazon or Netflix or iTunes Genius and it recommends the movies or products and music to you,thats 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 lerarn by itself to customize(定製；定做) itself to your preferences.Finally learning algorithms are being used today to understand human learning and to understand the brain. We 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 slightly older article at the top of this list of the twelve most desirable IT skills was machine learning. Here at Stanford the number of recruiters(僱主) contact me asking if I know any graduateing machine learning students is far larger than student here. So I think there is a vast unfullfilled 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, well start to give a more formal definition of what is machine learning, and well begin to talk about the main types of machine learning problems and algorithms. Youll pick up some of the main machine learning terminology(術語), and start to get a sense of what are the different algorithms and when each one might be appropriate