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These are the user uploaded subtitles that are being translated: 1 00:00:01,010 --> 00:00:03,240 Machine learning is creating 2 00:00:03,240 --> 00:00:05,430 tremendous economic value today. 3 00:00:05,430 --> 00:00:08,430 I think 99 percent of the economic value 4 00:00:08,430 --> 00:00:09,870 created by machine learning today 5 00:00:09,870 --> 00:00:11,955 is through one type of machine learning, 6 00:00:11,955 --> 00:00:14,025 which is called supervised learning. 7 00:00:14,025 --> 00:00:16,470 Let's take a look at what that means. 8 00:00:16,470 --> 00:00:18,900 Supervised machine learning or 9 00:00:18,900 --> 00:00:21,075 more commonly, supervised learning, 10 00:00:21,075 --> 00:00:24,480 refers to algorithms that learn x to 11 00:00:24,480 --> 00:00:28,590 y or input to output mappings. 12 00:00:28,590 --> 00:00:31,770 The key characteristic of supervised learning is 13 00:00:31,770 --> 00:00:33,225 that you give 14 00:00:33,225 --> 00:00:36,680 your learning algorithm examples to learn from. 15 00:00:36,680 --> 00:00:40,730 That includes the right answers, whereby right answer, 16 00:00:40,730 --> 00:00:45,235 I mean, the correct label y for a given input x, 17 00:00:45,235 --> 00:00:48,915 and is by seeing correct pairs of 18 00:00:48,915 --> 00:00:52,850 input x and desired output label y that 19 00:00:52,850 --> 00:00:54,890 the learning algorithm eventually learns to 20 00:00:54,890 --> 00:00:57,050 take just the input alone without 21 00:00:57,050 --> 00:00:58,970 the output label and gives 22 00:00:58,970 --> 00:01:03,185 a reasonably accurate prediction or guess of the output. 23 00:01:03,185 --> 00:01:05,620 Let's look at some examples. 24 00:01:05,620 --> 00:01:08,010 If the input x is 25 00:01:08,010 --> 00:01:11,985 an email and the output y is this email, 26 00:01:11,985 --> 00:01:13,590 spam or not spam, 27 00:01:13,590 --> 00:01:16,785 this gives you your spam filter. 28 00:01:16,785 --> 00:01:21,120 Or if the input is an audio clip and 29 00:01:21,120 --> 00:01:25,565 the algorithm's job is output the text transcript, 30 00:01:25,565 --> 00:01:29,470 then this is speech recognition. 31 00:01:29,470 --> 00:01:32,770 Or if you want to input English and have 32 00:01:32,770 --> 00:01:35,515 it output to corresponding Spanish, 33 00:01:35,515 --> 00:01:37,495 Arabic, Hindi, Chinese, Japanese, 34 00:01:37,495 --> 00:01:39,445 or something else translation, 35 00:01:39,445 --> 00:01:42,385 then that's machine translation. 36 00:01:42,385 --> 00:01:46,000 Or the most lucrative form of supervised learning 37 00:01:46,000 --> 00:01:50,110 today is probably used in online advertising. 38 00:01:50,110 --> 00:01:53,560 Nearly all the large online ad platforms have 39 00:01:53,560 --> 00:01:56,950 a learning algorithm that inputs some information about 40 00:01:56,950 --> 00:01:59,815 an ad and some information about you 41 00:01:59,815 --> 00:02:01,825 and then tries to figure out 42 00:02:01,825 --> 00:02:04,120 if you will click on that ad or not. 43 00:02:04,120 --> 00:02:05,970 Because by showing you ads they're 44 00:02:05,970 --> 00:02:07,970 just slightly more likely to click on, 45 00:02:07,970 --> 00:02:09,910 for these large online ad platforms, 46 00:02:09,910 --> 00:02:11,710 every click is revenue, 47 00:02:11,710 --> 00:02:13,360 this actually drives a lot of 48 00:02:13,360 --> 00:02:15,070 revenue for these companies. 49 00:02:15,070 --> 00:02:18,055 This is something I once done a lot of work on, 50 00:02:18,055 --> 00:02:20,665 maybe not the most inspiring application, 51 00:02:20,665 --> 00:02:23,560 but it certainly has a significant economic impact 52 00:02:23,560 --> 00:02:25,195 in some countries today. 53 00:02:25,195 --> 00:02:28,870 Or if you want to build a self-driving car, 54 00:02:28,870 --> 00:02:31,180 the learning algorithm would take as input 55 00:02:31,180 --> 00:02:33,715 an image and some information from 56 00:02:33,715 --> 00:02:35,665 other sensors such as a radar or 57 00:02:35,665 --> 00:02:39,775 other things and then try to output the position of, 58 00:02:39,775 --> 00:02:41,320 say, other cars so that 59 00:02:41,320 --> 00:02:42,700 your self-driving car can 60 00:02:42,700 --> 00:02:45,230 safely drive around the other cars. 61 00:02:45,230 --> 00:02:47,565 Or take manufacturing. 62 00:02:47,565 --> 00:02:49,190 I've actually done a lot of work in 63 00:02:49,190 --> 00:02:51,835 this sector at learning AI. 64 00:02:51,835 --> 00:02:54,050 You can have a learning algorithm takes as 65 00:02:54,050 --> 00:02:57,395 input a picture of a manufactured product, 66 00:02:57,395 --> 00:03:01,415 say a cell phone that just rolled off the production line 67 00:03:01,415 --> 00:03:03,680 and have the learning algorithm output 68 00:03:03,680 --> 00:03:05,845 whether or not there is a scratch, 69 00:03:05,845 --> 00:03:08,715 dent, or other defect in the product. 70 00:03:08,715 --> 00:03:11,600 This is called visual inspection and it's helping 71 00:03:11,600 --> 00:03:13,340 manufacturers reduce or prevent 72 00:03:13,340 --> 00:03:15,410 defects in their products. 73 00:03:15,410 --> 00:03:17,750 In all of these applications, 74 00:03:17,750 --> 00:03:20,465 you will first train your model with examples of 75 00:03:20,465 --> 00:03:23,480 inputs x and the right answers, 76 00:03:23,480 --> 00:03:25,670 that is the labels y. 77 00:03:25,670 --> 00:03:28,340 After the model has learned from these input, 78 00:03:28,340 --> 00:03:30,365 output, or x and y pairs, 79 00:03:30,365 --> 00:03:33,065 they can then take a brand new input x, 80 00:03:33,065 --> 00:03:34,505 something it has never seen before, 81 00:03:34,505 --> 00:03:36,110 and try to produce the 82 00:03:36,110 --> 00:03:39,815 appropriate corresponding output y. 83 00:03:39,815 --> 00:03:44,420 Let's dive more deeply into one specific example. 84 00:03:44,420 --> 00:03:46,565 Say you want to predict 85 00:03:46,565 --> 00:03:49,730 housing prices based on the size of the house. 86 00:03:49,730 --> 00:03:52,280 You've collected some data and say 87 00:03:52,280 --> 00:03:55,390 you plot the data and it looks like this. 88 00:03:55,390 --> 00:03:57,485 Here on the horizontal axis 89 00:03:57,485 --> 00:03:59,825 is the size of the house in square feet. 90 00:03:59,825 --> 00:04:01,730 Yes, I live in the United States 91 00:04:01,730 --> 00:04:03,155 where we still use square feet. 92 00:04:03,155 --> 00:04:06,160 I know most of the world uses square meters. 93 00:04:06,160 --> 00:04:10,430 Here on the vertical axis is the price of the house in, 94 00:04:10,430 --> 00:04:12,820 say, thousands of dollars. 95 00:04:12,820 --> 00:04:16,850 With this data, let's say a friend wants to know what's 96 00:04:16,850 --> 00:04:21,095 the price for their 750 square foot house. 97 00:04:21,095 --> 00:04:23,485 How can the learning algorithm help you? 98 00:04:23,485 --> 00:04:25,130 One thing a learning algorithm 99 00:04:25,130 --> 00:04:26,615 might be able to do is say, 100 00:04:26,615 --> 00:04:28,280 for the straight line to 101 00:04:28,280 --> 00:04:31,700 the data and reading off the straight line, 102 00:04:31,700 --> 00:04:33,350 it looks like your friend's house could 103 00:04:33,350 --> 00:04:35,225 be sold for maybe about, 104 00:04:35,225 --> 00:04:38,320 I don't know, $150,000. 105 00:04:38,320 --> 00:04:40,670 But fitting a straight line isn't 106 00:04:40,670 --> 00:04:42,830 the only learning algorithm you can use. 107 00:04:42,830 --> 00:04:44,390 There are others that could work 108 00:04:44,390 --> 00:04:46,280 better for this application. 109 00:04:46,280 --> 00:04:49,160 For example, routed and fitting a straight line, 110 00:04:49,160 --> 00:04:52,460 you might decide that it's better to fit a curve, 111 00:04:52,460 --> 00:04:54,320 a function that's slightly more 112 00:04:54,320 --> 00:04:58,010 complicated or more complex than a straight line. 113 00:04:58,010 --> 00:05:00,950 If you do that and make a prediction here, 114 00:05:00,950 --> 00:05:02,645 then it looks like, well, 115 00:05:02,645 --> 00:05:07,725 your friend's house could be sold for closer to $200,000. 116 00:05:07,725 --> 00:05:11,225 One of the things you see later in this class 117 00:05:11,225 --> 00:05:14,240 is how you can decide whether to fit a straight line, 118 00:05:14,240 --> 00:05:15,620 a curve, or 119 00:05:15,620 --> 00:05:20,310 another function that is even more complex to the data. 120 00:05:20,310 --> 00:05:22,870 Now, it doesn't seem appropriate to pick 121 00:05:22,870 --> 00:05:25,440 the one that gives your friend the best price, 122 00:05:25,440 --> 00:05:27,790 but one thing you see is how to get 123 00:05:27,790 --> 00:05:30,400 an algorithm to systematically 124 00:05:30,400 --> 00:05:32,680 choose the most appropriate line or 125 00:05:32,680 --> 00:05:36,445 curve or other thing to fit to this data. 126 00:05:36,445 --> 00:05:38,770 What you've seen in this slide is 127 00:05:38,770 --> 00:05:41,335 an example of supervised learning. 128 00:05:41,335 --> 00:05:43,660 Because we gave the algorithm a dataset in 129 00:05:43,660 --> 00:05:45,910 which the so-called right answer, 130 00:05:45,910 --> 00:05:47,980 that is the label or 131 00:05:47,980 --> 00:05:52,220 the correct price y is given for every house on the plot. 132 00:05:52,220 --> 00:05:54,820 The task of the learning algorithm is to 133 00:05:54,820 --> 00:05:57,295 produce more of these right answers, 134 00:05:57,295 --> 00:05:59,440 specifically predicting what is 135 00:05:59,440 --> 00:06:00,640 the likely price for 136 00:06:00,640 --> 00:06:03,445 other houses like your friend's house. 137 00:06:03,445 --> 00:06:06,275 That's why this is supervised learning. 138 00:06:06,275 --> 00:06:08,750 To define a little bit more terminology, 139 00:06:08,750 --> 00:06:10,940 this housing price prediction is 140 00:06:10,940 --> 00:06:12,920 the particular type of supervised learning 141 00:06:12,920 --> 00:06:14,635 called regression. 142 00:06:14,635 --> 00:06:17,030 By regression, I mean we're trying 143 00:06:17,030 --> 00:06:19,115 to predict a number from 144 00:06:19,115 --> 00:06:21,410 infinitely many possible numbers 145 00:06:21,410 --> 00:06:24,590 such as the house prices in our example, 146 00:06:24,590 --> 00:06:28,035 which could be 150,000 or 147 00:06:28,035 --> 00:06:33,645 70,000 or 183,000 or any other number in between. 148 00:06:33,645 --> 00:06:37,020 That's supervised learning, learning input, 149 00:06:37,020 --> 00:06:39,620 output, or x to y mappings. 150 00:06:39,620 --> 00:06:42,020 You saw in this video an example of 151 00:06:42,020 --> 00:06:45,620 regression where the task is to predict number. 152 00:06:45,620 --> 00:06:48,845 But there's also a second major type of 153 00:06:48,845 --> 00:06:52,190 supervised learning problem called classification. 154 00:06:52,190 --> 00:06:55,890 Let's take a look at what that means in the next video.11048

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