10 - 2 - Logistic Regression Error (15-58)
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10 - 1 - Logistic Regression Problem (14-33)
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9 - 4 - Linear Regression for Binary Classification (11-23)
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8 - 2 - Error Measure (15-10)
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9 - 2 - Linear Regression Algorithm (20-03)
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8 - 3 - Algorithmic Error Measure (13-46)
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9 - 1 - Linear Regression Problem (10-08)
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13 Jacobian
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7 - 1 - Definition of VC Dimension (13-10)
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8 - 4 - Weighted Classification (16-54)
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32 Computational Methods2
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15 - 4 - V-Fold Cross Validation (10-41)
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15 - 2 - Validation (13-24)
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14 - 1 - Regularized Hypothesis Set (19-16)
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10 - 4 - Gradient Descent (19-18)(1)
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1 基本概念
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28 Fisher判别分析
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14 - 4 - General Regularizers (13-28)
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6 - 4 - A Pictorial Proof (16-01)
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5 - 1 - Recap and Preview (13-44)
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1 - 1 - Course Introduction (10-58)(1)
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27 Matr-x Completion
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13 核主元分析
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12 - 4 - Structured Hypothesis Sets (09-36)
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15 - 1 - Model Selection Problem (16-00)
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9 - 3 - Generalization Issue (20-34)
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4 - 2 - Probability to the Rescue (11-33)
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10 核定义
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40 SVM
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16 - 1 - Occam-'s Razor (10-08)
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13 - 3 - Deterministic Noise (14-07)
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41 Boosting1
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8 - 1 - Noise and Probabilistic Target (17-01)
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5 - 2 - Effective Number of Lines (15-26)
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7 - 4 - Interpreting VC Dimension (17-13)
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6 - 2 - Bounding Function- Basic Cases (06-56)
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11 正定核性质
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37 Naive Bayes方法
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12 - 1 - Quadratic Hypothesis (23-47)
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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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16 - 3 - Data Snooping (12-28)