2 - 1 - Model Representation (8 min).srt

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Our first learning algorithm will be linear regression. In this video, you'll see
我们的第一个学习算法将 线性回归。在这段视频中，你会看到
(字幕整理：中国海洋大学 黄海广,haiguang2000@qq.com )

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what the model looks like and more importantly you'll see what the overall
什么样的模型看起来像多 重要的是，你会看到什么整体

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process of supervised learning looks like. Let's use some motivating example of predicting
监督学习过程中的模样。让我们 使用一些激励的例子预测

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housing prices. We're going to use a data set of housing prices from the city of
住房价格上涨。我们将使用数据 从城市的住房价格设置

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Portland, Oregon. And here I'm gonna plot my data set of a number of houses
俄勒冈州波特兰市。在这里，我要去 绘制数据集的一些房屋

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that were different sizes that were sold for a range of different prices. Let's say
不同尺寸已售出 对于不同的价格范围内。比方说，

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that given this data set, you have a friend that's trying to sell a house and
这组数据，你有一个 朋友，试图把房子卖了，

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let's see if friend's house is size of 1250 square feet and you want to tell them
让我们来看看，如果朋友的房子是大小 1250平方英尺，你要告诉他们

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how much they might be able to sell the house for. Well one thing you could do is
他们也许能卖多少 房子。好了，你可以做的一件事是

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fit a model. Maybe fit a straight line to this data. Looks something like that and based
拟合模型。也许适合直线 此数据。看起来是这样的，根据

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on that, maybe you could tell your friend that let's say maybe he can sell the
，也许你可以告诉你的朋友 比方说，也许他可以卖

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house for around $220,000. So this is an example of a
房子周围220,000元。 因此，这是一个例子的

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supervised learning algorithm. And it's supervised learning because we're given
监督的学习算法。和它的 因为我们的监督学习

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the, quotes, "right answer" for each of our examples. Namely we're told what was
，报价，“正确的答案”为每个 我们的例子。即告诉我们是什么

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the actual house, what was the actual price of each of the houses in our data
实际的房子，什么是实际 每个房子的价格在我们的数据

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set were sold for and moreover, this is an example of a regression problem where
集已售出，而且，这是 的一个例子的回归问题中

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the term regression refers to the fact that we are predicting a real-valued output
回归一词是指这样的事实 我们预测一个真正的值输出

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namely the price. And just to remind you the other most common type of supervised
即价格。只是提醒你 其他监督的最常见的类型

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learning problem is called the classification problem where we predict
学习问题被称为 分类问题，我们预测

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discrete-valued outputs such as if we are looking at cancer tumors and trying to
比如，如果我们的离散值输出 看癌症肿瘤，并试图

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decide if a tumor is malignant or benign. So that's a zero-one valued discrete output. More
决定如果肿瘤是良性或恶性。 所以这是一个零一值离散输出。更多

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formally, in supervised learning, we have a data set and this data set is called a
正式监督学习，我们有 数据集并在这样的数据组被称为一个

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training set. So for housing prices example, we have a training set of
训练集。因此，住房价格 例如，我们有一个训练集

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different housing prices and our job is to learn from this data how to predict prices
不同的房价和我们的工作是 从这个数据中学习如何预测价格

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of the houses. Let's define some notation that we're using throughout this course.
的房子。让我们来定义一些符号 我们正在使用的整个过程。

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We're going to define quite a lot of symbols. It's okay if you don't remember
我们要定义颇多 符号。没关系，如果你不记得

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all the symbols right now but as the course progresses it will be useful
所有的符号，但现在作为 课程的进展，将是有益的

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[inaudible] convenient notation. So I'm gonna use lower case m throughout this course to
[听不清]方便的符号。所以，我会使用 整个本课程小写米

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denote the number of training examples. So in this data set, if I have, you know,
培训的例子的数字表示。所以 在这组数据中，如果我有，你知道，

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let's say 47 rows in this table. Then I have 47 training examples and m equals 47.
让我们说，在此表中的47列。然后我 有47个训练实例和m等于47。

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Let me use lowercase x  to denote the input variables often also called the
让我用小写字母x表示 输入变量经常也被称为

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features. That would be the x is here, it would the input features. And I'm gonna
功能。这将是x是在这里，它会输入功能。我要去

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use y to denote my output variables or the target variable which I'm going to
用y来表示我的输出变量或 目标变量，我要去

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predict and so that's the second column here. [inaudible] notation, I'm
预测，所以这是第二 列在这里。 [听不清]符号，我

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going to use (x, y) to denote a single training example. So, a single row in this
要使用（X，Y）来表示一个单 培训的例子。所以，在此单排

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table corresponds to a single training example and to refer to a specific
表对应一个单一的培训 的例子，并参照特定的

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training example, I'm going to use this notation x(i) comma gives me y(i) And, we're
培训例子中，我将使用这个 符号X（I）逗号给了我Y（I），我们

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going to use this to refer to the ith training example. So this superscript i
要利用这个参阅第i个 培训的例子。所以这个下标i

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over here, this is not exponentiation right? This (x(i), y(i)), the superscript i in
在这里，这是不求幂 对不对？ （X（I），Y（I）），上标i

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parentheses that's just an index into my training set and refers to the ith row in
那只是一个索引我的括号 培训是指第i行

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this table, okay? So this is not x to the power of i, y to the power of i. Instead
此表，好吗？所以这不是X到 电源I，Y i的功率。代替

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(x(i), y(i)) just refers to the ith row of this table. So for example, x(1) refers to the
（×（i）中，Y（I）），在此指的是第i行 表中。因此，例如，x（1）指的是

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input value for the first training example so that's 2104. That's this x in the first
输入值的第一次训练的例子，所以 那是2104。这是这个x在第一

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row. x(2) will be equal to 1416 right? That's the second x
一行。 ×（2）将等于 1416吧？这是第二个X

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and y(1) will be equal to 460. The first, the y value for my first
和y（1）将等于460。 第一，我的第一个y值

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training example, that's what that (1) refers to. So as mentioned, occasionally I'll ask you a
培训的例子，这就是：（1） 指。所以提到的，偶尔我会问你一个

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question to let you check your understanding and a few seconds in this
质疑让你检查你的 理解，在这几秒钟

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video a multiple-choice question will pop up in the video. When it does,
视频选择题 会弹出视频。当它，

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please use your mouse to select what you think is the right answer. What defined by
请使用鼠标来选择你 我认为是正确的答案。定义的

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the training set is. So here's how this supervised learning algorithm works.
训练集。因此，这里是如何 监督学习算法的工作原理。

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We saw that with the training set like our training set of housing prices and we feed
我们看到，像我们的训练集 训练集的住房价格，我们养活

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that to our learning algorithm. Is the job of a learning algorithm to then output a
就我们的学习算法。是对工作 学习算法，然后输出

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function which by convention is usually denoted lowercase h and h
按照约定的功能 通常表示小写h和h

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stands for hypothesis And what the job of the hypothesis is, is, is a function that
看台假说和什么样的工作， 的假设是，是，是一个函数，

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takes as input the size of a house like maybe the size of the new house your friend's
作为输入那样的房子的大小 也许你的朋友的新房子的大小

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trying to sell so it takes in the value of x and it tries to output the estimated
挂羊头卖狗肉，所以它需要的价值 x和它试图输出估计

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value of y for the corresponding house. So h is a function that maps from x's
y值对应的房子。 因此，h是从x的一个??函数，映射

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to y's. People often ask me, you know, why is this function called
y的。人们经常问我，你 知道了，这是为什么函数调用

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hypothesis. Some of you may know the meaning of the term hypothesis, from the
假说。你们有些人可能知道 这意味着，长期假设，从

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dictionary or from science or whatever. It turns out that in machine learning, this
字典或从科学或什么的。它 原来，在机器学习，这

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is a name that was used in the early days of machine learning and it kinda stuck. 'Cause
在初期使用的名称 机器学习和它有点卡住。因为

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maybe not a great name for this sort of function, for mapping from sizes of
也许不是一个伟大的名字为这种 功能，从尺寸的映射

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houses to the predictions, that you know.... I think the term hypothesis, maybe isn't
房子的预言，你知道.... 我认为长期假设，也许是不

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the best possible name for this, but this is the standard terminology that people use in
此最好的可能的名称，但是这是 标准术语的人使用

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machine learning. So don't worry too much about why people call it that. When
机器学习。所以不要太担心 人们为什么称呼它。何时

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designing a learning algorithm, the next thing we need to decide is how do we
设计一个学习算法，下一个 需要决定的事情，我们是怎么做的，我们

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represent this hypothesis h. For this and the next few videos, I'm going to choose
代表这假设h。为了这个以及 接下来的几个视频，我要选择

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our initial choice , for representing the hypothesis, will be the following. We're going to
我们最初的选择，代表 假设，会出现下面的。我们要

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represent h as follows. And we will write this as h<u>theta(x) equals theta<u>0</u></u>
表示h为如下。我们将这样写： ?<U>西塔（X）等于THETA <U> 0 </ U> </ U>

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plus theta<u>1 of x. And as a shorthand, sometimes instead of writing, you</u>
加θ<U>来的x 1。而作为一个缩写， 有时，而不是写作，你</ U>

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know, h subscript theta of x, sometimes there's a shorthand, I'll just write as a h of x.
知道标西塔，H，X，有时 有一个速记，我就写一个h的x。

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But more often I'll write it as a subscript theta over there. And plotting
但更多的时候我会写它作为一个 标西塔那边。和绘图

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this in the pictures, all this means is that, we are going to predict that y is a linear
这在图片中，所有这一切意味着， 我们将要预测，y是一个线性

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function of x. Right, so that's the data set and what this function is doing,
x的函数。没错，所以这是 数据集，这个功能是做什么，

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is predicting that y is some straight line function of x. That's h of x equals theta 0
预测y是一些直 直线x的函数。这是x的?等于THETA 0

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plus theta 1 x, okay? And why a linear function? Well, sometimes we'll want to
加THETA 1个，好吗？为什么线性 功能？嗯，有时候我们会想

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fit more complicated, perhaps non-linear functions as well. But since this linear
适应更加复杂，或许非线性 功能。但是，由于这种线性

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case is the simple building block, we will start with this example first of fitting
案件是简单的积木，我们将 从这个例子，首先拟合

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linear functions, and we will build on this to eventually have more complex
线性函数，我们将建立 这最终有更复杂的

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models, and more complex learning algorithms. Let me also give this
模型，以及更复杂的学习 算法。让我也给这个

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particular model a name. This model is called linear regression or this, for
特定型号的名称。这种模式是 称为线性回归，

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example, is actually linear regression with one variable, with the variable being
例如，实际上是线性回归 一个变量，该变量是

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x. Predicting all the prices as functions of one variable X. And another name for
x的所有的价格预测功能 一个变量X的另一个名字

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this model is univariate linear regression. And univariate is just a
这种模式是单变量线性 回归。单因素仅仅是一个

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fancy way of saying one variable. So, that's linear regression. In the next
奇特的方式说一个变量。因此， 这就是线性回归。在接下来的

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video we'll start to talk about just how we go about implementing this model.
我们将开始谈论多么视频 我们去实施这一模式。