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林轩田机器学习基石（Machine Learning Foundation）

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2018-11-25 15:50:01

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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》。《机器学习基石》课程由浅入深、内容全面，基本涵盖了机器学习领域的很多方面。其作为机器学习的入门和进阶资料非常适合。

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1 - 2 - What is Machine Learning (18-28)

18:30

1 - 3 - Applications of Machine Learning (18-56)

18:58

1 - 4 - Components of Machine Learning (11-45)

11:46

1 - 5 - Machine Learning and Other Fields (10-21)

10:22

2 - 1 - Perceptron Hypothesis Set (15-42)

15:43

2 - 2 - Perceptron Learning Algorithm (PLA) (19-46)

19:47

2 - 3 - Guarantee of PLA (12-37)

12:38

2 - 4 - Non-Separable Data (12-55)

12:56

3 - 1 - Learning with Different Output Space (17-26)

17:27

3 - 2 - Learning with Different Data Label (18-12)

18:13

3 - 3 - Learning with Different Protocol (11-09)

11:10

3 - 4 - Learning with Different Input Space (14-13)

14:14

4 - 1 - Learning is Impossible- (13-32)

13:33

4 - 2 - Probability to the Rescue (11-33)

11:34

4 - 3 - Connection to Learning (16-46)

16:47

4 - 4 - Connection to Real Learning (18-06)

18:06

5 - 1 - Recap and Preview (13-44)

13:45

5 - 2 - Effective Number of Lines (15-26)

15:27

5 - 3 - Effective Number of Hypotheses (16-17)

16:18

5 - 4 - Break Point (07-44)

07:45

6 - 1 - Restriction of Break Point (14-18)

14:19

6 - 2 - Bounding Function- Basic Cases (06-56)

06:57

6 - 3 - Bounding Function- Inductive Cases (14-47)

14:48

6 - 4 - A Pictorial Proof (16-01)

16:02

7 - 1 - Definition of VC Dimension (13-10)

13:11

7 - 2 - VC Dimension of Perceptrons (13-27)

13:28

7 - 3 - Physical Intuition of VC Dimension (6-11)

06:12

7 - 4 - Interpreting VC Dimension (17-13)

17:14

8 - 1 - Noise and Probabilistic Target (17-01)

17:02

8 - 2 - Error Measure (15-10)

15:11

8 - 3 - Algorithmic Error Measure (13-46)

13:48

8 - 4 - Weighted Classification (16-54)

16:55

9 - 2 - Linear Regression Algorithm (20-03)

20:03

9 - 3 - Generalization Issue (20-34)

20:35

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

11:24

10 - 2 - Logistic Regression Error (15-58)

15:59

10 - 3 - Gradient of Logistic Regression Error (15-38)

15:38

10 - 4 - Gradient Descent (19-18)

19:19

11 - 2 - Stochastic Gradient Descent (11-39)

11:40

11 - 3 - Multiclass via Logistic Regression (14-18)

14:19

11 - 4 - Multiclass via Binary Classification (11-35)

11:36

12 - 1 - Quadratic Hypothesis (23-47)

23:48

12 - 2 - Nonlinear Transform (09-52)

09:53

12 - 3 - Price of Nonlinear Transform (15-37)

15:38

12 - 4 - Structured Hypothesis Sets (09-36)

09:37

13 - 1 - What is Overfitting- (10-45)

10:46

13 - 2 - The Role of Noise and Data Size (13-36)

13:36

13 - 3 - Deterministic Noise (14-07)

14:08

13 - 4 - Dealing with Overfitting (10-49)

10:50

14 - 2 - Weight Decay Regularization (24-08)

24:09

14 - 3 - Regularization and VC Theory (08-15)

08:16

14 - 4 - General Regularizers (13-28)

13:29

15 - 1 - Model Selection Problem (16-00)

16:01

15 - 2 - Validation (13-24)

13:25

15 - 3 - Leave-One-Out Cross Validation (16-06)

16:07

15 - 4 - V-Fold Cross Validation (10-41)

10:42

16 - 2 - Sampling Bias (11-50)

11:51

16 - 3 - Data Snooping (12-28)

12:29

16 - 4 - Power of Three (08-49)

08:51

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