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In this video I am going to define what is probably the most common type of machine learning problem, which is supervised learning. I'll define supervised learning more formally later, but it's probably best to explain or start with an example of what it is and we'll do the formal definition later. Let's say you want to predict housing prices. A while back, a student collected data sets from the Institute of Portland Oregon. And let's say you plot a data set and it looks like this. Here on the horizontal axis, the size of different houses in square feet, and on the vertical axis, the price of different houses in thousands of dollars. So. Given this data, let's say you have a friend who owns a house that is, say 750 square feet and hoping to sell the house and they want to know how much they can get for the house. So how can the learning algorithm help you? One thing a learning algorithm might be able to do is put a straight line through the data or to fit a straight line to the data and, based on that, it looks like maybe the house can be sold for maybe about $150,000. But maybe this isn't the only learning algorithm you can use. There might be a better one. For example, instead of sending a straight line to the data, we might decide that it's better to fit a quadratic function or a second-order polynomial to this data. And if you do that, and make a prediction here, then it looks like, well, maybe we can sell the house for closer to $200,000. One of the things we'll talk about later is how to choose and how to decide do you want to fit a straight line to the data or do you want to fit the quadratic function to the data and there's no fair picking whichever one gives your friend the better house to sell. But each of these would be a fine example of a learning algorithm. So this is an example of a supervised learning algorithm. And the term supervised learning refers to the fact that we gave the algorithm a data set in which the "right answers" were given. That is, we gave it a data set of houses in which for every example in this data set, we told it what is the right price so what is the actual price that, that house sold for and the toss of the algorithm was to just produce more of these right answers such as for this new house, you know, that your friend may be trying to sell. To define with a bit more terminology this is also called a regression problem and by regression problem I mean we're trying to predict a continuous value output. Namely the price. So technically I guess prices can be rounded off to the nearest cent. So maybe prices are actually discrete values, but usually we think of the price of a house as a real number, as a scalar value, as a continuous value number and the term regression refers to the fact that we're trying to predict the sort of continuous values attribute. Here's another supervised learning example, some friends and I were actually working on this earlier. Let's see you want to look at medical records and try to predict of a breast cancer as malignant or benign. If someone discovers a breast tumor, a lump in their breast, a malignant tumor is a tumor that is harmful and dangerous and a benign tumor is a tumor that is harmless. So obviously people care a lot about this.

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