【高级】机器学习基石台湾大学视频教程 66课

长乐混混

课程介绍：

非常不错的机器学习课程，课程介绍各领域中机器学习的算法、理论及使用工具，课程中使用中文讲解，会使用到英文投影，但不会涉及比较深的英文。

课程目录：

文档资料
01 - 1 - Course Introduction (10_58).mp4
01 - 2 - What is Machine Learning (18_28).mp4
01 - 3 - Applications of Machine Learning (18-56).mp4
01 - 4 - Components of Machine Learning (11_45).mp4
01 - 5 - Machine Learning and Other Fields (10_21).mp4
02 - 1 - Perceptron Hypothesis Set (15-42).mp4
02 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp4
02 - 3 - Guarantee of PLA (12_37).mp4
02 - 4 - Non-Separable Data (12-55).mp4
03 - 1 - Learning with Different Output Space (17-26).mp4
03 - 2 - Learning with Different Data Label (18-12).mp4
03 - 3 - Learning with Different Protocol (11-09).mp4
03 - 4 - Learning with Different Input Space (14-13).mp4
04 - 1 - Learning is Impossible- (13-32).mp4
04 - 2 - Probability to the Rescue (11_33).mp4
04 - 3 - Connection to Learning (16_46).mp4
04 - 4 - Connection to Real Learning (18-06).mp4
05 - 1 - Recap and Preview (13-44).mp4
05 - 2 - Effective Number of Lines (15-26).mp4
05 - 3 - Effective Number of Hypotheses (16-17).mp4
05 - 4 - Break Point (07-44).mp4
06 - 1 - Restriction of Break Point (14-18).mp4
06 - 2 - Bounding Function- Basic Cases (06-56).mp4
06 - 3 - Bounding Function- Inductive Cases (14-47).mp4
06 - 4 - A Pictorial Proof (16-01).mp4
07 - 1 - Definition of VC Dimension (13-10).mp4
07 - 2 - VC Dimension of Perceptrons (13-27).mp4
07 - 3 - Physical Intuition of VC Dimension (6-11).mp4
07 - 4 - Interpreting VC Dimension (17_13).mp4
08 - 1 - Noise and Probabilistic Target (17-01).mp4
08 - 2 - Error Measure (15-10).mp4
08 - 3 - Algorithmic Error Measure (13_46).mp4
08 - 4 - Weighted Classification (16-54).mp4
09 - 1 - Linear Regression Problem (10-08).mp4
09 - 2 - Linear Regression Algorithm (20-03).mp4
09 - 3 - Generalization Issue (20-34).mp4
09 - 4 - Linear Regression for Binary Classification (11_2...
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).m...
10 - 4 - Gradient Descent (19-18).mp4
11 - 1 - Linear Models for Binary Classification (21-35)....
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
NTU ML Techniques Trailer.mp4

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- 4 - Power of Three (08-49).mp4
NTU ML Techniques Trailer.mp4

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