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林轩田机器学习技法（Machine Learning Techniques）

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2018-11-25 21:41:39

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台湾大学林轩田老师曾在coursera上开设了两门机器学习经典课程：《机器学习基石》和《机器学习技法》。《机器学习技法》课程主要介绍了机器学习领域经典的一些算法，包括支持向量机、决策树、随机森林、神经网络等等。林老师的教学风格也很幽默风趣，总让读者在轻松愉快的氛围中掌握知识。更多机器学习干货资源，请关注微信公众号：AI有道！

红色石头Will 发消息

1 - 1 - Course Introduction (4-07)

04:09

1 - 2 - Large-Margin Separating Hyperplane (14-17)

14:18

1 - 3 - Standard Large-Margin Problem (19-16)

19:17

1 - 4 - Support Vector Machine (15-33)

15:34

1 - 5 - Reasons behind Large-Margin Hyperplane (13-31)

13:32

2 - 1 - Motivation of Dual SVM (15-54)

15:55

2 - 2 - Lagrange Dual SVM (18-50)

18:51

2 - 3 - Solving Dual SVM (14-19)

14:20

2 - 4 - Messages behind Dual SVM (11-18)

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)

17:30

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)

20:38

12 - 2 - Neural Network Hypothesis (18-01)

18:03

12 - 3 - Neural Network Learning (20-15)

20:16

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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