Machine learning lecture 1  (Page 3/17)

Turns out that most of you probably use learning algorithms — I don't know — I think half a dozen times a day or maybe a dozen times a day or more, and often without knowing it. So, for example, every time you send mail via the US Postal System, turns out there's an algorithm that tries to automatically read the zip code you wrote on your envelope, and that's done by a learning algorithm. So every time you send US mail, you are using a learning algorithm, perhaps without even being aware of it.

Similarly, every time you write a check, I actually don't know the number for this, but a significant fraction of checks that you write are processed by a learning algorithm that's learned to read the digits, so the dollar amount that you wrote down on your check. So every time you write a check, there's another learning algorithm that you're probably using without even being aware of it.

If you use a credit card, or I know at least one phone company was doing this, and lots of companies like eBay as well that do electronic transactions, there's a good chance that there's a learning algorithm in the background trying to figure out if, say, your credit card's been stolen or if someone's engaging in a fraudulent transaction.

If you use a website like Amazon or Netflix that will often recommend books for you to buy or movies for you to rent or whatever, these are other examples of learning algorithms that have learned what sorts of things you like to buy or what sorts of movies you like to watch and can therefore give customized recommendations to you.

Just about a week ago, I had my car serviced, and even there, my car mechanic was trying to explain to me some learning algorithm in the innards of my car that's sort of doing its best to optimize my driving performance for fuel efficiency or something.

So, see, most of us use learning algorithms half a dozen, a dozen, maybe dozens of times without even knowing it.

And of course, learning algorithms are also doing things like giving us a growing understanding of the human genome. So if someday we ever find a cure for cancer, I bet learning algorithms will have had a large role in that. That's sort of the thing that Tom works on, yes?

So in teaching this class, I sort of have three goals. One of them is just to I hope convey some of my own excitement about machine learning to you.

The second goal is by the end of this class, I hope all of you will be able to apply state-of-the-art machine learning algorithms to whatever problems you're interested in. And if you ever need to build a system for reading zip codes, you'll know how to do that by the end of this class.

And lastly, by the end of this class, I realize that only a subset of you are interested in doing research in machine learning, but by the conclusion of this class, I hope that all of you will actually be well qualified to start doing research in machine learning, okay?

So let's say a few words about logistics. The prerequisites of this class are written on one of the handouts, are as follows: In this class, I'm going to assume that all of you have sort of basic knowledge of computer science and knowledge of the basic computer skills and principles. So I assume all of you know what big?O notation, that all of you know about sort of data structures like queues, stacks, binary trees, and that all of you know enough programming skills to, like, write a simple computer program. And it turns out that most of this class will not be very programming intensive, although we will do some programming, mostly in either MATLAB or Octave. I'll say a bit more about that later.