图解教材概率机器学习(Murphy)

『课程目录』:   3 z' j7 l( S! J" x" _! q
1.教材与笔记的针对人群
2.如何理解probabilistic approach
3.为什么要学习机器学习和采用probabilistic approach
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4.如何理解大数据和它的long tail
5.监督学习supervised learning 快速梳理
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6.无监督学习unsupervised learning 快速梳理
7.强化学习reinforcement learning 快速梳理
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8.监督学习案例抽象提炼
9.classification快速梳理
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10.从概率角度描述分类模型
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11.分类问题常见案例
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12.从概率角度简单梳理unsupervised learning
13.从概率角度简单梳理clustering
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14.简单梳理latent factors dimension reductio
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15.简单梳理graph structure
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16.简单梳理matrix completion图片填补电影推荐购物清单
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17.什么是parametric nonparametric model
18.从概率角度理解KNN
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19.什么是parametric nonparametric model
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20.如何用gaussian distribution 来描述linear和no
21.如何用bernoulli distribution 来描述logistic
22.什么是overfitting
23.如何理解model selection
24.如何理解No free lunch theorem
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25.对于概率的2种理解方式
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26.什么是discrete random variable PMF indic
27.概率的fundamental rules
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28.如何理解bayes theorem和cancer检测结果的误解分析
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29.generative classifier 与discriminative
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30.什么是unconditional 和 conditional indepe
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31.如何理解continous random variable CDF PDF
32.从概率分布角度理解quantiles
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33.从概率角度理解均值方差标准差
34.如何理解binomial and bernoulli distributi
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35.简单理解multinomial和multinoulli distribut
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36.如何理解poisson distribution
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37.简单梳理empirical distribution
38.简单梳理gaussian distribution
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39.简单梳理degenerate pdf 和student t distrib
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40.简单梳理laplace distribution
41.简单梳理gamma distribution
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42.简单梳理beta distribution
43.简单梳理pareto distribution
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44.简单梳理joint probability distribution
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45.简单梳理covariance and correlation
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46.简单梳理multivariate gaussian
47.简单梳理dirichlet distribution
48.简单梳理linear transformation
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49.简单梳理general transformation
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50.简单梳理multivariate change of variables
51.简单梳理central limit theorem
52.简单梳理Monte Carlo Approximation
53.简单梳理Monte Carlo Approximation on pi
54.简单梳理accuracy of Monte Carlo Approxima
55.简单梳理information theory and ML
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56.简单理解entropy
57.简单理解KL divergence
58.简单理解mutual information
59.简单理解mutual information on continuous 
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60.第三章generative models for discrete dat
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61.Bayesian Concept Learning只用positive e
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62.likelihood 和 occam razor帮助挑选最优模型
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63.Prior的能力 on hypotheses space和rapid le
64.如何理解posterior likelihood 和prior之间的关系
65.什么是posterior predictive distribution
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66.什么是complex prior
67.beta binomial model 开启
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68.likelihood on Bernoulli and Binomial
69.什么是beta distribution
70.粗略理解posterior on binomial and beta di
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71.posterior mean and mode与prior和likelih
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72.简单理解Beta posterior variance

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