Machine learning lecture 1  (Page 14/17)

And so my goal in this class, running through the entire quarter, not just on learning theory, is actually not only to give you the tools of machine learning, but to teach you how to use them well. And I've noticed this is something that really not many other classes teach. And this is something I'm really convinced is a huge deal, and so by the end of this class, I hope all of you will be master carpenters. I hope all of you will be really good at applying these learning algorithms and getting them to work amazingly well in many problems. Okay?

Let's see. So [inaudible] the board. After learning theory, there's another class of learning algorithms that I then want to teach you about, and that's unsupervised learning. So you recall, right, a little earlier I drew an example like this, right, where you have a couple of features, a couple of input variables and sort of malignant tumors and benign tumors or whatever. And that was an example of a supervised learning problem because the data you have gives you the right answer for each of your patients. The data tells you this patient has a malignant tumor; this patient has a benign tumor. So it had the right answers, and you wanted the algorithm to just produce more of the same.

In contrast, in an unsupervised learning problem, this is the sort of data you get, okay? Where speaking loosely, you're given a data set, and I'm not gonna tell you what the right answer is on any of your data. I'm just gonna give you a data set and I'm gonna say, "Would you please find interesting structure in this data set?" So that's the unsupervised learning problem where you're sort of not given the right answer for everything.

So, for example, an algorithm may find structure in the data in the form of the data being partitioned into two clusters, or clustering is sort of one example of an unsupervised learning problem.

So I hope you can see this. It turns out that these sort of unsupervised learning algorithms are also used in many problems. This is a screen shot — this is a picture I got from Sue Emvee, who's a PhD student here, who is applying unsupervised learning algorithms to try to understand gene data, so is trying to look at genes as individuals and group them into clusters based on properties of what genes they respond to — based on properties of how the genes respond to different experiments.

Another interesting application of [inaudible] sorts of clustering algorithms is actually image processing, this which I got from Steve Gules, who's another PhD student. It turns out what you can do is if you give this sort of data, say an image, to certain unsupervised learning algorithms, they will then learn to group pixels together and say, gee, this sort of pixel seems to belong together, and that sort of pixel seems to belong together.

And so the images you see on the bottom — I guess you can just barely see them on there — so the images you see on the bottom are groupings — are what the algorithm has done to group certain pixels together. On a small display, it might be easier to just look at the image on the right. The two images on the bottom are two sort of identical visualizations of the same grouping of the pixels into [inaudible] regions.