林轩田机器学习基石（Machine Learning Foundation）
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。
视频选集
（1/59）
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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