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NTU: Machine Learning Techniques 机器学习技法 by 林軒田

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2017-04-05 22:13:25
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详见 https://www.yuque.com/ob26eq/nshoar/xsu1la
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01 Course Introduction 4-07
04:08
02 Large-Margin Separating Hyperplane 14-17
14:18
03 Standard Large-Margin Problem 19-16
19:18
04 Support Vector Machine 15-33
15:34
05 Reasons behind Large-Margin Hyperplane 13-31
13:32
01 Motivation of Dual SVM 15-54
15:55
02 Lagrange Dual SVM 18-50
18:51
03 Solving Dual SVM 14-19
14:20
04 Messages behind Dual SVM 11-18
11:19
01 Kernel Trick 20-23
20:24
02 Polynomial Kernel 12-16
12:17
03 Gaussian Kernel 14-43
14:44
04 Comparison of Kernels 13-35
13:36
01 Motivation and Primal Problem 14-27
06:39
02 Dual Problem 7-38
04:30
03 Messages behind Soft-Margin SVM 13-44
13:45
04 Model Selection 9-57
09:58
01 Soft-Margin SVM as Regularized Model 13-40
13:41
02 SVM versus Logistic Regression 10-18
10:19
03 SVM for Soft Binary Classification 9-36
09:37
04 Kernel Logistic Regression 16-22
16:23
01 Kernel Ridge Regression 17-17
17:18
02 Support Vector Regression Primal 18-44
18:45
03 Support Vector Regression Dual 13-05
13:06
04 Summary of Kernel Models 09-06
09:06
01 Motivation of Aggregation 18-54
18:56
02 Uniform Blending 20-31
20:32
03 Linear and Any Blending 16-48
16:49
04 Bagging Bootstrap Aggregation 11-48
11:49
01 Motivation of Boosting 12-47
12:48
02 Diversity by Re-weighting 14-28
14:29
03 Adaptive Boosting Algorithm 13-34
13:35
04 Adaptive Boosting in Action 11-04
11:05
01 Decision Tree Hypothesis 17-28
17:29
02 Decision Tree Algorithm 15-20
15:21
03 Decision Tree Heuristics in CRT 13-21
13:22
04 Decision Tree in Action 8-44
08:45
01 Random Forest Algorithm 13-06
13:07
02 Out-Of-Bag Estimate 12-31
12:33
03 Feature Selection 19-27
19:28
04 Random Forest in Action13-28
13:29
01 Adaptive Boosted Decision Tree 15-05
15:07
02 Optimization View of AdaBoost 27-25
27:26
03 Gradient Boosting 18-20
18:21
04 Summary of Aggregation Models 11-19
11:19
01 Motivation 20-36
20:38
02 Neural Network Hypothesis 18-01
18:02
03 Neural Network Learning 22-26
22:26
04 Optimization and Regularization 17-29
17:30
13 - 1 - Deep Neural Network (21-30)
21:31
13 - 2 - Autoencoder (15-17)
15:18
13 - 3 - Denoising Autoencoder (8-30)
08:31
13 - 4 - Principal Component Analysis (31-20)
31:22
14 - 1 - RBF Network Hypothesis (12-55)
12:56
14 - 2 - RBF Network Learning (20-08)
20:09
14 - 3 - k-Means Algorithm (16-19)
16:20
14 - 4 - k-Means and RBF Network in Action (9-46)
09:47
15 - 1 - Linear Network Hypothesis (20-16)
20:17
15 - 2 - Basic Matrix Factorization (16-32)
16:33
15 - 3 - Stochastic Gradient Descent (12-22)
12:23
15 - 4 - Summary of Extraction Models (9-12)
09:13
16 - 1 - Feature Exploitation Techniques (16-11)
16:12
16 - 2 - Error Optimization Techniques (8-40)
08:40
16 - 3 - Overfitting Elimination Techniques (6-44)
06:44
16 - 4 - Machine Learning in Action (12-59)
13:00

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