视频选集
（1/65）
自动连播
1 - 1 - Course Introduction
04:08
1 - 2 - Large-Margin Separating Hyperplane
14:18
1 - 3 - Standard Large-Margin Problem
19:17
1 - 4 - Support Vector Machine
15:34
1 - 5 - Reasons behind Large-Margin Hyperplane
13:32
2 - 1 - Motivation of Dual SVM
15:55
2 - 2 - Lagrange Dual SVM
18:51
2 - 3 - Solving Dual SVM
14:20
2 - 4 - Messages behind Dual SVM
11:19
3 - 1 - Kernel Trick (20-23)
20:24
3 - 2 - Polynomial Kernel (12-16)
12:17
3 - 3 - Gaussian Kernel (14-43)
14:44
3 - 4 - Comparison of Kernels (13-35)
13:36
4 - 1 - Motivation and Primal Problem (14-27)
14:29
4 - 2 - Dual Problem (7-38)
07:39
4 - 3 - Messages behind Soft-Margin SVM (13-44)
13:45
4 - 4 - Model Selection (9-57)
09:58
5 - 1 - Soft-Margin SVM as Regularized Model (13-40)
13:42
5 - 2 - SVM versus Logistic Regression (10-18)
10:20
5 - 3 - SVM for Soft Binary Classification (9-36)
09:38
5 - 4 - Kernel Logistic Regression (16-22)
16:23
6 - 1 - Kernel Ridge Regression (17-17)
17:18
6 - 2 - Support Vector Regression Primal (18-44)
18:45
6 - 3 - Support Vector Regression Dual (13-05)
13:06
6 - 4 - Summary of Kernel Models (09-06)
09:07
7 - 1 - Motivation of Aggregation (18-54)
18:55
7 - 2 - Uniform Blending (20-31)
20:32
7 - 3 - Linear and Any Blending (16-48)
16:49
7 - 4 - Bagging (Bootstrap Aggregation) (11-48)
11:49
8 - 1 - Motivation of Boosting (12-47)
12:48
8 - 2 - Diversity by Re-weighting (14-28)
14:29
8 - 3 - Adaptive Boosting Algorithm (13-34)
13:35
8 - 4 - Adaptive Boosting in Action (11-04)
11:06
9 - 1 - Decision Tree Hypothesis (17-28)
17:29
9 - 2 - Decision Tree Algorithm (15-20)
15:21
9 - 3 - Decision Tree Heuristics in C--&RT (13-21)
13:22
9 - 4 - Decision Tree in Action (8-44)
08:45
10 - 1 - Random Forest Algorithm (13-06)
13:07
10 - 2 - Out-Of-Bag Estimate (12-31)
12:33
10 - 3 - Feature Selection (19-27)
19:28
10 - 4 - Random Forest in Action(13-28)
13:29
11 - 1 - Adaptive Boosted Decision Tree (15-05)
15:07
11 - 2 - Optimization View of AdaBoost (27-25)
27:26
11 - 3 - Gradient Boosting (18-20)
18:21
11 - 4 - Summary of Aggregation Models (11-19)
11:20
12 - 1 - Motivation (20-36)
20:37
12 - 2 - Neural Network Hypothesis (18-01)
18:02
12 - 3 - Neural Network Learning (22-26)
22:27
12 - 4 - 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)
08:32
13 - 4 - Principal Component Analysis (31-20)
31:21
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)
08:41
16 - 3 - Overfitting Elimination Techniques (6-44)
06:45
16 - 4 - Machine Learning in Action (12-59)
13:00
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