文件名称:Machine-Learning
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斯坦福大学的Andrew Ng讲述的机器学习课程讲义-Stanford Andrew Ng tells the machine learning course notes
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下载文件列表
notes01_Linear Regression; Classification and logistic regression; Generalized Linear Models.pdf
notes02_Generative Learning algorithms( Gaussian discriminant analysis; Naive Bayes Classifier).pdf
notes03_Support Vector Machines.pdf
notes04_Learning Theory.pdf
notes05_Regularization and model selection.pdf
notes06_The perceptron and large margin classifers.pdf
notes07a_The k-means clustering algorithm.pdf
notes07b_Mixtures of Gaussians and the EM algorithm.pdf
notes08_The EM algorithm.pdf
notes09_Factor analysis.pdf
notes10_Principal components analysis.pdf
notes11_Independent Components Analysis.pdf
notes12_Reinforcement Learning and Control.pdf
notes02_Generative Learning algorithms( Gaussian discriminant analysis; Naive Bayes Classifier).pdf
notes03_Support Vector Machines.pdf
notes04_Learning Theory.pdf
notes05_Regularization and model selection.pdf
notes06_The perceptron and large margin classifers.pdf
notes07a_The k-means clustering algorithm.pdf
notes07b_Mixtures of Gaussians and the EM algorithm.pdf
notes08_The EM algorithm.pdf
notes09_Factor analysis.pdf
notes10_Principal components analysis.pdf
notes11_Independent Components Analysis.pdf
notes12_Reinforcement Learning and Control.pdf
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