076 第76讲 无监督学习.mp4

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章节2：EM算法和GMM高斯混合模型 https://www.aliyundrive.com/s/CQtQSsPHcii/folder/641e9c27b869fcd2a2164b09b39b60f3d94c8a5d folder

章节1：聚类系列算法 https://www.aliyundrive.com/s/CQtQSsPHcii/folder/641e9c286d886a8a7d4c4befa20131cbd1577e2c folder

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06 其他参数与总结.mp4 102.20 MB mp4

05 模型评估指标轮廓系数.mp4 184.03 MB mp4

04 sklearn实现.mp4 129.61 MB mp4

03 python实现.mp4 164.15 MB mp4

02 簇内平方和的定义.mp4 126.16 MB mp4

01 基础原理.mp4 123.11 MB mp4

06 其他参数与总结.mp4 102.20 MB mp4

05 模型评估指标轮廓系数.mp4 184.03 MB mp4

04 sklearn实现.mp4 129.61 MB mp4

03 python实现.mp4 164.15 MB mp4

02 簇内平方和的定义.mp4 126.16 MB mp4

01 基础原理.mp4 123.11 MB mp4

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21.9KNN(K最近邻)算法（一）.mp4 36.21 MB mp4

21.8监督学习框架（二）.mp4 42.39 MB mp4

21.7监督学习框架（一）.mp4 32.13 MB mp4

21.6超参数（二）.mp4 26.77 MB mp4

21.5超参数（一）.mp4 34.29 MB mp4

21.4lasso回归.mp4 45.80 MB mp4

21.3正则化.mp4 18.82 MB mp4

21.2最大后验估计.mp4 42.58 MB mp4

21.1经验误差和泛化误差.mp4 43.26 MB mp4

21.14高斯判别模型（二）.mp4 34.18 MB mp4

21.13高斯判别模型（一）.mp4 23.77 MB mp4

21.12线性分类器.mp4 28.38 MB mp4

21.11KNN(K最近邻)算法（三）.mp4 19.84 MB mp4

21.10KNN(K最近邻)算法（二）.mp4 39.83 MB mp4

21.9KNN(K最近邻)算法（一）.mp4 36.21 MB mp4

21.8监督学习框架（二）.mp4 42.39 MB mp4

21.7监督学习框架（一）.mp4 32.13 MB mp4

21.6超参数（二）.mp4 26.77 MB mp4

21.5超参数（一）.mp4 34.29 MB mp4

21.4lasso回归.mp4 45.80 MB mp4

21.3正则化.mp4 18.82 MB mp4

21.2最大后验估计.mp4 42.58 MB mp4

21.1经验误差和泛化误差.mp4 43.26 MB mp4

21.14高斯判别模型（二）.mp4 34.18 MB mp4

21.13高斯判别模型（一）.mp4 23.77 MB mp4

21.12线性分类器.mp4 28.38 MB mp4

21.11KNN(K最近邻)算法（三）.mp4 19.84 MB mp4

21.10KNN(K最近邻)算法（二）.mp4 39.83 MB mp4