8 - 1 - Noise and Probabilistic Target (17-01)
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13 - 3 - Deterministic Noise (14-07)
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6 - 4 - A Pictorial Proof (16-01)
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29 谱聚类1
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32 Computational Methods2
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8 - 4 - Weighted Classification (16-54)
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42 Boosting2
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10 核定义
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13 - 4 - Dealing with Overfitting (10-49)
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8 - 3 - Algorithmic Error Measure (13-46)
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13 Jacobian
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01 概率基础
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02 随机变量1
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11 - 1 - Linear Models for Binary Classification (21-35)
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13 - 1 - What is Overfitting- (10-45)
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16 - 2 - Sampling Bias (11-50)
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12 正定核应用
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27 Matr-x Completion
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25 spectral clustering
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1 基本概念
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34 Kernel FDA
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5 - 2 - Effective Number of Lines (15-26)
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2 - 3 - Guarantee of PLA (12-37)
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11 - 2 - Stochastic Gradient Descent (11-39)
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13 核主元分析
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13 - 2 - The Role of Noise and Data Size (13-36)
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26 K-means algorithm
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9 - 3 - Generalization Issue (20-34)
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12 - 4 - Structured Hypothesis Sets (09-36)
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15 主坐标分析
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16 - 1 - Occam-'s Razor (10-08)
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14 主元分析
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15 - 2 - Validation (13-24)
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6 条件期望
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3 - 1 - Learning with Different Output Space (17-26)
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1 - 4 - Components of Machine Learning (11-45)(1)
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10 - 4 - Gradient Descent (19-18)(1)
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6 - 3 - Bounding Function- Inductive Cases (14-47)
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14 - 4 - General Regularizers (13-28)
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40 SVM
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38 Support Vector Machines1