印象笔记同步分享:Machine Learning-Mixtures of Gaussians and the EM algorithm…

网易公开课,第12,13课 notes,7a, 7b,8 从这章开始,介绍无监督的算法 对于无监督,当然首先想到k means, 最典型也最简单,有需要直接看7a的讲义   Mixtures of Gaussians 如果要理解Mixtures of Gaussians,那先回去复习一下Gaussians Discriminant Analysis,高斯判别分析 首先高斯判别分析是生成算法, 所以不会直接拟合p(y|x), 而是拟合p(x|y)p(y), 即p(x,y) p(y)符合伯努力分布,…

http://cs229.stanford.edu/ http://cs229.stanford.edu/notes/cs229-notes7b.pdf…

这篇讨论使用期望最大化算法(Expectation-Maximization)来进行密度估计(density estimation). 与k-means一样,给定的训练样本是,我们将隐含类别标签用表示.与k-means的硬指定不同,我们首先认为是满足一定的概率分布的,这里我们认为满足多项式分布,,其中,有k个值{1,…,k}可以选取.而且我们认为在给定后,满足多值高斯分布,即.由此可以得到联合分布. 整个模型简单描述为对于每个样例,我们先从k个类别中按多项式分布抽取一个,然后根据所对应的k个多值…

1    Unsupervised Learning 1.1    k-means clustering algorithm 1.1.1    算法思想 1.1.2    k-means的不足之处 1.1.3    如何选择K值 1.1.4    Spark MLlib 实现 k-means 算法 1.2    Mixture of Gaussians and the EM algorithm 1.3    The EM Algorithm 1.4    Principal Components…

Machine Learning Algorithms Study Notes 高雪松 @雪松Cedro Microsoft MVP 目 录 1    Introduction    1 1.1    What is Machine Learning    1 1.2    学习心得和笔记的框架    1 2    Supervised Learning    3 2.1    Perceptron Learning Algorithm (PLA)    3 2.1.1    PLA -- "知…

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644   How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…

A Gentle Guide to Machine Learning Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed. Got it? We can make machines lear…

EM算法,全称Expectation Maximization Algorithm,译作最大期望化算法或期望最大算法,是机器学习十大算法之一,吴军博士在<数学之美>书中称其为“上帝视角”算法,其重要性可见一斑. EM算法是一种迭代算法,用于含有隐变量(hidden variable)的概率参数模型的最大似然估计或极大后验概率估计.它与极大似然估计的区别就是它在迭代过程中依赖极大似然估计方法.极大似然估计是在模型已知的情况下,求解模型的参数$\theta$,让抽样出现的概率最大.类似于求解一元方…

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