Okay, hi Michael.>>Hey Charles, how’s it going.>>It’s going pretty well, how’s it going in your end of the world?>>Very nice, what are we want to talk about today?>>Well today we are going to talk about supervised learning. But, in particular what we’re going to talk about are two kinds of supervised learning, and one particular way to do supervised learning. Okay, so the two types of supervised learning that we typically think about are classification. And regression. And we’re going to spend most of the time today talking about classification and more time next time talking about regression. So the difference between classification and regression is fairly simple for the purposes of this discussion. Classification is simply the process of taking some kind of input, let’s call it x. And I’m going to define these terms in a couple of minutes. And mapping it to some discrete label. Usually, for what we’re talking about, something like, true or false. So, what’s a good example of that? Imagine that I have a nice little picture of Michael.>>It looks just like me!>>It looks exactly like you. So I have a nice little picture here and I want to know whether this is a male. Or a female. So given an input like this I will map it to male or female. So what do you think, Michael? Do you think this is a male or a female?>>So you’re, you’re classifying me as male or female based on the picture of me and I would think you know, based on how I look I’m clearly male.>>Yes. In fact, manly male. So, this would be a classification from pictures to male. The alternative would be something like a picture to female, and I’m just going to take a completely stereotypical image of either a female or.>>I think it’s actually, that’s actually me when I let my hair go long.>>Right, so, so which points out that this can be pretty hard. But this is where we’re going to spend most of our time talking about it first as a classification task. So taking some kind of input, in this case pictures, and mapping it to some discrete number of labels, true or false, male or female, car versus cougar, anything that, that you might imagine thinking of.>>Car versus cougar?>>Yes.>>That, I guess that’s an important thing if you’re driving. You don’t want to run into any cougars or probably other cars either.>>Well you know, you’re sitting down and you’re trying to decide whether you should ride this thing that you see or not.>>And if its a cougar maybe you don’t want to and if it’s a car maybe you do.>>Excellent. Don’t drive a cougar.>>Don’t drive a cougar. That’s the first lesson in machine learning.>>Excellent.>>Okay, so that’s classification. We’ll return to regression in a little bit later during this conversation. But, just as a preview, regression is more about continuous value function. So, something like giving a bunch of points. I want to give in a new point. I want to map it to some real value. So we may pretend that these are examples of a line and so given a point here, I might say the output is right there. Okay, so that’s regression but we’ll talk about that in a moment. Right now, what I want to talk about is classification.>>Would an example of regression also be, for example, mapping the pictures of me to the length of my hair? Like a number that represents the length of my hair?>>Absolutely, for the purposes of, of the sort of things that we’re going to be worried about you can really think of the difference between classification and regression is the difference between mapping from some input to some small number of discrete values which might represent concepts. And regression is mapping from some input space to some real number. Potentially infinite number of real numbers.>>Cool, let’s do a, let’s do a quiz. Make sure we get this.>>Okay, I like that.