14 - 4 - General Regularizers (13-28)
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13 Jacobian
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5 - 1 - Recap and Preview (13-44)
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28 Fisher判别分析
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8 - 3 - Algorithmic Error Measure (13-46)
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10 - 1 - Logistic Regression Problem (14-33)
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16 - 3 - Data Snooping (12-28)
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11 - 4 - Multiclass via Binary Classification (11-35)
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14 - 2 - Weight Decay Regularization (24-08)
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13 核主元分析
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3 - 4 - Learning with Different Input Space (14-13)
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4 - 2 - Probability to the Rescue (11-33)
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14 - 1 - Regularized Hypothesis Set (19-16)
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10 - 3 - Gradient of Logistic Regression Error (15-38)
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41 Boosting1
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25 spectral clustering
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35 Linear classification1
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4 - 1 - Learning is Impossible- (13-32)
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1 - 2 - What is Machine Learning (18-28)
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31 Computational Methods1
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7 - 2 - VC Dimension of Perceptrons (13-27)
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8 - 4 - Weighted Classification (16-54)
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7 - 4 - Interpreting VC Dimension (17-13)
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15 - 1 - Model Selection Problem (16-00)
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11 - 2 - Stochastic Gradient Descent (11-39)
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6 - 1 - Restriction of Break Point (14-18)
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11 - 1 - Linear Models for Binary Classification (21-35)
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40 SVM
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12 - 4 - Structured Hypothesis Sets (09-36)
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17 概率PCA
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27 Matr-x Completion
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8 - 1 - Noise and Probabilistic Target (17-01)
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34 Kernel FDA
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2 - 3 - Guarantee of PLA (12-37)
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9 - 3 - Generalization Issue (20-34)
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10 - 2 - Logistic Regression Error (15-58)
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01-PCL教程-官网关于和入门指南-滤波器和特征
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8 - 2 - Error Measure (15-10)
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6 - 2 - Bounding Function- Basic Cases (06-56)
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9 - 4 - Linear Regression for Binary Classification (11-23)
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36 Linear classification2