台湾大学林轩田 机器学习基石(887.84M)

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『课程目录』:
    1 - 1 - Course Introduction (10-58).mp4
    1 - 2 - What is Machine Learning (18-28).mp4
    1 - 3 - Applications of Machine Learning (18-56).mp4
    1 - 4 - Components of Machine Learning (11-45).mp4
    1 - 5 - Machine Learning and Other Fields (10-21).mp4
    10 - 1 - Logistic Regression Problem (14-33).mp4
    10 - 2 - Logistic Regression Error (15-58).mp4
    10 - 3 - Gradient of Logistic Regression Error (15-38).mp4
    10 - 4 - Gradient Descent (19-18).mp4
    11 - 1 - Linear Models for Binary Classification (21-35).mp4
    11 - 2 - Stochastic Gradient Descent (11-39).mp4
    11 - 3 - Multiclass via Logistic Regression (14-18).mp4
    11 - 4 - Multiclass via Binary Classification (11-35).mp4
    12 - 1 - Quadratic Hypothesis (23-47).mp4
    12 - 2 - Nonlinear Transform (09-52).mp4
    12 - 3 - Price of Nonlinear Transform (15-37).mp4
    12 - 4 - Structured Hypothesis Sets (09-36).mp4
    13 - 1 - What is Overfitting- (10-45).mp4
    13 - 2 - The Role of Noise and Data Size (13-36).mp4
    13 - 3 - Deterministic Noise (14-07).mp4
    13 - 4 - Dealing with Overfitting (10-49).mp4
    14 - 1 - Regularized Hypothesis Set (19-16).mp4
    14 - 2 - Weight Decay Regularization (24-08).mp4
    14 - 3 - Regularization and VC Theory (08-15).mp4
    14 - 4 - General Regularizers (13-28).mp4
    15 - 1 - Model Selection Problem (16-00).mp4
    15 - 2 - Validation (13-24).mp4
    15 - 3 - Leave-One-Out Cross Validation (16-06).mp4
    15 - 4 - V-Fold Cross Validation (10-41).mp4
    16 - 1 - Occam-'s Razor (10-08).mp4
    16 - 2 - Sampling Bias (11-50).mp4
    16 - 3 - Data Snooping (12-28).mp4
    16 - 4 - Power of Three (08-49).mp4
    2 - 1 - Perceptron Hypothesis Set (15-42).mp4
    2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp4
    2 - 3 - Guarantee of PLA (12-37).mp4
    2 - 4 - Non-Separable Data (12-55).mp4
    3 - 1 - Learning with Different Output Space (17-26).mp4
    3 - 2 - Learning with Different Data Label (18-12).mp4
    3 - 3 - Learning with Different Protocol (11-09).mp4
    3 - 4 - Learning with Different Input Space (14-13).mp4
    4 - 1 - Learning is Impossible- (13-32).mp4
    4 - 2 - Probability to the Rescue (11-33).mp4
    4 - 3 - Connection to Learning (16-46).mp4
    4 - 4 - Connection to Real Learning (18-06).mp4
    5 - 1 - Recap and Preview (13-44).mp4
    5 - 2 - Effective Number of Lines (15-26).mp4
    5 - 3 - Effective Number of Hypotheses (16-17).mp4
    5 - 4 - Break Point (07-44).mp4
    6 - 1 - Restriction of Break Point (14-18).mp4
    6 - 2 - Bounding Function- Basic Cases (06-56).mp4
    6 - 3 - Bounding Function- Inductive Cases (14-47).mp4
    6 - 4 - A Pictorial Proof (16-01).mp4
    7 - 1 - Definition of VC Dimension (13-10).mp4
    7 - 2 - VC Dimension of Perceptrons (13-27).mp4
    7 - 3 - Physical Intuition of VC Dimension (6-11).mp4
    7 - 4 - Interpreting VC Dimension (17-13).mp4
    8 - 1 - Noise and Probabilistic Target (17-01).mp4
    8 - 2 - Error Measure (15-10).mp4
    8 - 3 - Algorithmic Error Measure (13-46).mp4
    8 - 4 - Weighted Classification (16-54).mp4
    9 - 1 - Linear Regression Problem (10-08).mp4
    9 - 2 - Linear Regression Algorithm (20-03).mp4
    9 - 3 - Generalization Issue (20-34).mp4
    9 - 4 - Linear Regression for Binary Classification (11-23).mp4

『课程下载』:

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     9 - 4 - Linear Regression for Binary Classification (11-23).mp4

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台湾大学林轩田 机器学习基石(887.84M)