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Subtitle preview

First 50 subtitle cues from .().eng.srt

50 cues

#100:00:02,033 --> 00:00:04,067
Our first learning algorithm will be linear regression. In this video, you'll see
#200:00:06,095 --> 00:00:09,023
what the model looks like and more importantly you'll see what the overall
#300:00:09,023 --> 00:00:14,080
process of supervised learning looks like. Let's use some motivating example of predicting
#400:00:14,080 --> 00:00:20,003
housing prices. We're going to use a data set of housing prices from the city of
#500:00:20,003 --> 00:00:25,020
Portland, Oregon. And here I'm gonna plot my data set of a number of houses
#600:00:25,020 --> 00:00:30,083
that were different sizes that were sold for a range of different prices. Let's say
#700:00:30,083 --> 00:00:35,087
that given this data set, you have a friend that's trying to sell a house and
#800:00:35,087 --> 00:00:41,023
let's see if friend's house is size of 1250 square feet and you want to tell them
#900:00:41,023 --> 00:00:46,045
how much they might be able to sell the house for. Well one thing you could do is
#1000:00:46,064 --> 00:00:53,003
fit a model. Maybe fit a straight line to this data. Looks something like that and based
#1100:00:53,003 --> 00:00:59,016
on that, maybe you could tell your friend that let's say maybe he can sell the
#1200:00:59,016 --> 00:01:03,057
house for around $220,000. So this is an example of a
#1300:01:03,057 --> 00:01:08,083
supervised learning algorithm. And it's supervised learning because we're given
#1400:01:08,083 --> 00:01:14,028
the, quotes, "right answer" for each of our examples. Namely we're told what was
#1500:01:14,028 --> 00:01:19,035
the actual house, what was the actual price of each of the houses in our data
#1600:01:19,035 --> 00:01:24,044
set were sold for and moreover, this is an example of a regression problem where
#1700:01:24,044 --> 00:01:29,054
the term regression refers to the fact that we are predicting a real-valued output
#1800:01:29,054 --> 00:01:34,058
namely the price. And just to remind you the other most common type of supervised
#1900:01:34,058 --> 00:01:39,000
learning problem is called the classification problem where we predict
#2000:01:39,000 --> 00:01:45,020
discrete-valued outputs such as if we are looking at cancer tumors and trying to
#2100:01:45,020 --> 00:01:52,003
decide if a tumor is malignant or benign. So that's a zero-one valued discrete output. More
#2200:01:52,003 --> 00:01:57,008
formally, in supervised learning, we have a data set and this data set is called a
#2300:01:57,008 --> 00:02:02,001
training set. So for housing prices example, we have a training set of
#2400:02:02,001 --> 00:02:07,038
different housing prices and our job is to learn from this data how to predict prices
#2500:02:07,038 --> 00:02:11,090
of the houses. Let's define some notation that we're using throughout this course.
#2600:02:11,090 --> 00:02:16,009
We're going to define quite a lot of symbols. It's okay if you don't remember
#2700:02:16,009 --> 00:02:20,007
all the symbols right now but as the course progresses it will be useful
#2800:02:20,007 --> 00:02:24,026
[inaudible] convenient notation. So I'm gonna use lower case m throughout this course to
#2900:02:24,026 --> 00:02:28,089
denote the number of training examples. So in this data set, if I have, you know,
#3000:02:28,089 --> 00:02:34,036
let's say 47 rows in this table. Then I have 47 training examples and m equals 47.
#3100:02:34,036 --> 00:02:39,049
Let me use lowercase x to denote the input variables often also called the
#3200:02:39,049 --> 00:02:44,028
features. That would be the x is here, it would the input features. And I'm gonna
#3300:02:44,028 --> 00:02:49,055
use y to denote my output variables or the target variable which I'm going to
#3400:02:49,055 --> 00:02:54,055
predict and so that's the second column here. [inaudible] notation, I'm
#3500:02:54,055 --> 00:03:05,074
going to use (x, y) to denote a single training example. So, a single row in this
#3600:03:05,074 --> 00:03:12,006
table corresponds to a single training example and to refer to a specific
#3700:03:12,006 --> 00:03:19,070
training example, I'm going to use this notation x(i) comma gives me y(i) And, we're
#3800:03:25,032 --> 00:03:30,093
going to use this to refer to the ith training example. So this superscript i
#3900:03:30,093 --> 00:03:37,086
over here, this is not exponentiation right? This (x(i), y(i)), the superscript i in
#4000:03:37,086 --> 00:03:44,087
parentheses that's just an index into my training set and refers to the ith row in
#4100:03:44,087 --> 00:03:51,062
this table, okay? So this is not x to the power of i, y to the power of i. Instead
#4200:03:51,062 --> 00:03:58,021
(x(i), y(i)) just refers to the ith row of this table. So for example, x(1) refers to the
#4300:03:58,021 --> 00:04:04,097
input value for the first training example so that's 2104. That's this x in the first
#4400:04:04,097 --> 00:04:11,068
row. x(2) will be equal to 1416 right? That's the second x
#4500:04:11,068 --> 00:04:17,038
and y(1) will be equal to 460. The first, the y value for my first
#4600:04:17,038 --> 00:04:24,052
training example, that's what that (1) refers to. So as mentioned, occasionally I'll ask you a
#4700:04:24,052 --> 00:04:28,034
question to let you check your understanding and a few seconds in this
#4800:04:28,034 --> 00:04:34,004
video a multiple-choice question will pop up in the video. When it does,
#4900:04:34,004 --> 00:04:40,036
please use your mouse to select what you think is the right answer. What defined by
#5000:04:40,036 --> 00:04:45,012
the training set is. So here's how this supervised learning algorithm works.