machine learning is creating tremendous economic value today I think 99 of the economic value created by Machine learning today is through one type of machine learning which is called supervised learning let's take a look at what that means supervised machine learning or more commonly supervised learning refers to algorithms that learn X to y or input to Output mappings the key characteristic of supervised learning is that you give your learning algorithm examples to learn from that includes the right answers whereby right answer I mean the correct label y for a given input X and is by seeing correct pairs of input X and desired output label y that the learning algorithm eventually learns to take just the input alone without the output label and gives a reasonably accurate prediction or guess of the output let's look at some examples if the input X is an email and the outputs Y is this email spam or not spam this gives you your spam filter or if the input is an audio clip and the algorithm's job is to Output the text transcript then this is speech recognition or if you want to input English and have it output the corresponding Spanish Arabic Hindi Chinese Japanese or something else translation then that's machine translation or the most lucrative form of supervised learning today is probably used in online advertising nearly all the large online ad platforms have a learning algorithm that inputs some information about an ad and some information about you and then tries to figure out if you will click on that ad or not because by showing you ads that you're slightly more likely to click on for these large online ad platforms every click is revenue this actually drives a lot of revenue for these companies this is something I've once done a lot of work on maybe not the most inspiring application but it certainly has a significant economic impact in some companies today or if you want to build a self-driving car the learning algorithm would take its input and image and some information from other sensors such as a radar or other things and then try to Output the position of say other chords so your self-driving car can safely drive around the other cars or tick manufacturing I've actually done a lot of work in this sector at Landing AI you can have a learning algorithm take this input a picture of a manufactured product say a cell phone that just wrote off the production line and have the learning algorithm output whether or not there is a scratch Dent or other defects in the product this is called visual inspection and is helping manufacturers reduce or prevent defects in their products in all of these applications you will first train your model with examples of inputs X and the right answers that is the labels y after the model has learned from these input outputs or X and Y pairs they can then take a brand new input X something that's never seen before and try to produce the appropriate corresponding output y let's dive more deeply into one specific example say you want to predict housing prices based on the size of a house you've collected some data and say you plot the data and it looks like this here on the horizontal axis is the size of the house in square feet and yes I live in the United States where we still use square feet I know most of the world uses square meters and here on the vertical axis is the price of the house in say thousands of dollars so with this data let's say a friend wants to know what's the price for their 750 square foot hose how can the learning algorithm help you one thing a learning algorithm might be able to do is say fit a straight line to the data and reading off the straight line it looks like your friend's house could be sold for maybe about I don't know 150 000 dollars but fitting a straight line isn't the only learning Arrow you can use there are others that could work better for this application for example routed and fitting a straight line you might decide that is better to fit a curve a function that's slightly more complicated or more complex than a straight line if you do that to make a prediction here then it looks like well your friend's house could be so for closer to two hundred thousand dollars one of the things you see later in this class is how you can decide whether to fit a straight line a curve or another function that is even more complex to the data now it doesn't seem appropriate to pick the one that gives your friend the best price but one thing you see is how to get an algorithm to systematically choose the most appropriate line or curve or other thing to fit to this data what you're seeing in this slide is an example of supervised learning because we gave the algorithm a data set in which the so-called right answer that is the label or the correct price Y is given for every house on the plot and the task of the learning algorithm is to produce more of these right answers specifically predicting what is the likely price for other houses like your friend's house that's why this is supervised learning to define a little bit more terminology this housing price prediction is a particular type of supervised learning called regression and by regression I mean we're trying to predict a number from infinitely many possible numbers such as the house prices in our example which could be 150 000 or 70 000 or 183 000 or any other number in between so that's supervised learning learning inputs output or X to Y mappings and you saw in this video an example of regression where the task is to predict a number but there's also a second major type of supervised learning problem called classification let's take a look at what that means in the next video