Machine learning is one of the most exciting recent technologies. And in this article, you learn about the state of the art and also gain practice implementing and deploying these algorithms yourself. You've probably used 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 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 article. In this article, 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 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 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 the industry and basic science. 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 whirring]
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 the 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 a 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 skillset, 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 article. 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.
So we are going to stop here today, till tomorrow a good day. Bye