Machine learning lecture 1  (Page 15/17)

And so it turns out that this sort of clustering algorithm or this sort of unsupervised learning algorithm, which learns to group pixels together, it turns out to be useful for many applications in vision, in computer vision image processing.

I'll just show you one example, and this is a rather cool one that two students, Ashutosh Saxena and Min Sun here did, which is given an image like this, right? This is actually a picture taken of the Stanford campus. You can apply that sort of clustering algorithm and group the picture into regions. Let me actually blow that up so that you can see it more clearly. Okay. So in the middle, you see the lines sort of grouping the image together, grouping the image into [inaudible]regions.

And what Ashutosh and Min did was they then applied the learning algorithm to say can we take this clustering and use it to build a 3D model of the world? And so using the clustering, they then had a learning algorithm try to learn what the 3D structure of the world looks like so that they could come up with a 3D model that you can sort of fly through, okay? Although many people used to think it's not possible to take a single image and build a 3D model, but using a learning algorithm and that sort of clustering algorithm is the first step. They were able to.

I'll just show you one more example. I like this because it's a picture of Stanford with our beautiful Stanford campus. So again, taking the same sort of clustering algorithms, taking the same sort of unsupervised learning algorithm, you can group the pixels into different regions. And using that as a pre-processing step, they eventually built this sort of 3D model of Stanford campus in a single picture. You can sort of walk into the ceiling, look around the campus. Okay? This actually turned out to be a mix of supervised and unsupervised learning, but the unsupervised learning, this sort of clustering was the first step.

So it turns out these sorts of unsupervised — clustering algorithms are actually routinely used for many different problems, things like organizing computing clusters, social network analysis, market segmentation, so if you're a marketer and you want to divide your market into different segments or different groups of people to market to them separately; even for astronomical data analysis and understanding how galaxies are formed. These are just a sort of small sample of the applications of unsupervised learning algorithms and clustering algorithms that we'll talk about later in this class.

Just one particularly cool example of an unsupervised learning algorithm that I want to tell you about. And to motivate that, I'm gonna tell you about what's called the cocktail party problem, which is imagine that you're at some cocktail party and there are lots of people standing all over. And you know how it is, right, if you're at a large party, everyone's talking, it can be sometimes very hard to hear even the person in front of you. So imagine a large cocktail party with lots of people. So the problem is, is that all of these people talking, can you separate out the voice of just the person you're interested in talking to with all this loud background noise?