Stanford University Andrew Ng Machine Learning Course in Chinese

中文笔记\(1)线性回归、logistic回归和一般回归.pdf
........\(10)主成分分析.pdf
........\(11)独立成分分析.pdf
........\(12)线性判别分析.pdf
........\(13)因子分析.pdf
........\(14)增强学习.pdf
........\(15)典型关联分析.pdf
........\(16)偏最小二乘法回归.pdf
........\(2)判别模型、生成模型与朴素贝叶斯方法.pdf
........\(3)支持向量机SVM（上）.pdf
........\(4)支持向量机SVM（下）.pdf
........\(5)规则化和模型选择.pdf
........\(6)K-means聚类算法.pdf
........\(7)混合高斯模型和EM算法.pdf
........\(8)EM算法.pdf
........\(9)在线学习.pdf
........\Andrew Ng 机器学习 笔记coursera ml notes.pdf
........\支持向量机.docx
........\支持向量机.pdf
........\支持向量机_改.pdf
........\支持向量机_最优解.pdf
........\朴素贝叶斯分类模型.doc
........\朴素贝叶斯模型_似然函数.doc
........\核函数.doc
........\牛顿算法_指数族_广义线性模型.pdf
中文笔记

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