Machine learning lecture 1  (Page 7/17)

There are a couple of projects on Netflix rankings using learning algorithms; a few medical robots; ones on segmenting [inaudible] to segmenting pieces of the body using learning algorithms; one on musical instrument detection; another on irony sequence alignment; and a few algorithms on understanding the brain neuroscience, actually quite a few projects on neuroscience; a couple of projects on undescending fMRI data on brain scans, and so on; another project on market makings, the financial trading. There was an interesting project on trying to use learning algorithms to decide what is it that makes a person's face physically attractive. There's a learning algorithm on optical illusions, and so on.

And it goes on, so lots of fun projects. And take a look, then come up with your own ideas. But whatever you find cool and interesting, I hope you'll be able to make machine learning a project out of it. Yeah, question?

Student : Are these group projects?

Instructor (Andrew Ng): Oh, yes, thank you.

Student : So how many people can be in a group?

Instructor (Andrew Ng): Right. So projects can be done in groups of up to three people. So as part of forming study groups, later today as you get to know your classmates, I definitely also encourage you to grab two other people and form a group of up to three people for your project, okay? And just start brainstorming ideas for now amongst yourselves. You can also come and talk to me or the TAs if you want to brainstorm ideas with us.

Okay. So one more organizational question. I'm curious, how many of you know MATLAB? Wow, cool, quite a lot. Okay. So as part of the — actually how many of you know Octave or have used Octave? Oh, okay, much smaller number.

So as part of this class, especially in the homeworks, we'll ask you to implement a few programs, a few machine learning algorithms as part of the homeworks. And most of those homeworks will be done in either MATLAB or in Octave, which is sort of — I know some people call it a free version of MATLAB, which it sort of is, sort of isn't.

So I guess for those of you that haven't seen MATLAB before, and I know most of you have, MATLAB is I guess part of the programming language that makes it very easy to write codes using matrices, to write code for numerical routines, to move data around, to plot data. And it's sort of an extremely easy to learn tool to use for implementing a lot of learning algorithms.

And in case some of you want to work on your own home computer or something if you don't have a MATLAB license, for the purposes of this class, there's also — [inaudible] write that down [inaudible]MATLAB — there's also a software package called Octave that you can download for free off the Internet. And it has somewhat fewer features than MATLAB, but it's free, and for the purposes of this class, it will work for just about everything.

So actually I, well, so yeah, just a side comment for those of you that haven't seen MATLAB before I guess, once a colleague of mine at a different university, not at Stanford, actually teaches another machine learning course. He's taught it for many years. So one day, he was in his office, and an old student of his from, like, ten years ago came into his office and he said, "Oh, professor, professor, thank you so much for your machine learning class. I learned so much from it. There's this stuff that I learned in your class, and I now use every day. And it's helped me make lots of money, and here's a picture of my big house."