Lecture 3 | Machine Learning (Stanford)

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学术发展中心提供
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早上好，欢迎回来。今天我们要讲第三课。
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这节课
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我会涉及这些内容，
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当中有些
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我今天涉及的内容，可能感觉有点跳，
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我的意思是话题和话题之间有点跳跃。但是，这都是依照这节课的教学大纲走的，并且保证了逻辑的连续性。
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上节课，我们谈到，
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线性回归，今天我要谈谈
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一种叫做局部权重回归的适应算法。这是一种非常强大的
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算法，也可能是我前一任导师最喜爱的
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机器学习算法。随后我们会
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讨论一种线性回归的概率解释，
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并且用其展开我们的第一个
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逻辑回归的分类算法；课程中
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会有一段小插曲来略微谈谈感知算法，
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详细的内容会在之后的课程中展开；
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如果
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时间允许，我希望可以讲讲牛顿法，一种用于
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Logistic插值回归法的算法。
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好了，先让我们回忆下上节课的内容，
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回一下我定义的符号，
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我用
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X^i，
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Y^i来表达第i个训练样本。
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当
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我们谈及线性回归
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或者一般最小二乘时，
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我们用这个去表示
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我假定的H，
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在输入值为X^i
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时的预测值。
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我假定的公式，
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系数由向量
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系数𝛳表示，
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这就等于对j求和的theta_j*x_j
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写作theta转置乘以X
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我们按习惯约定X_0等于1
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对应我们线性回归模型里的
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截距项
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这里的小写n
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是我用的一个记号
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来表示训练集合中特征的数量
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所以在我们预测房屋售价的例子中，
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Title:: Lecture 3 | Machine Learning (Stanford)
Description:: more » « less
Video Language:: English
Duration:: 01:13:14

	Lu Ezra edited Chinese, Simplified subtitles for Lecture 3 \| Machine Learning (Stanford)
	Jialin Liu edited Chinese, Simplified subtitles for Lecture 3 \| Machine Learning (Stanford)
	Oops edited Chinese, Simplified subtitles for Lecture 3 \| Machine Learning (Stanford)

Chinese, Simplified subtitles

Incomplete

Revisions

Revision 3 Edited

Lu Ezra

Lecture 3 | Machine Learning (Stanford)

Revisions

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