林軒田(Hsuan-Tien Lin) - [機器學習基石]Machine Learning Foundations -
課程對應到台灣大學所開設之「機器學習」課程的前半學期，課程的強度比照世界一流大學的實體課程設計，希望能幫大家打下紥實的基礎，所以在份量上會比常見的線上課程要吃重一些，建議大家要預留充足的時間來把這門課學好。即使有前面這句話的「警告」，這門課在台大依然有著很好的口碑，因此 這幾年一直看到同學們「前仆後繼」地來修習，希望能和台大的同學一樣喜愛這門課程。
讲师:林軒田(Hsuan-Tien Lin)
视频选集
（10/65）
1 - 1 - Course Introduction
10:58
1 - 2 - What is Machine Learning
18:29
1 - 3 - Applications of Machine Learning
18:57
1 - 4 - Components of Machine Learning
11:45
1 - 5 - Machine Learning and Other Fields
10:21
2 - 1 - Perceptron Hypothesis Set
15:42
2 - 2 - Perceptron Learning Algorithm (PLA)
19:46
2 - 3 - Guarantee of PLA
12:37
2 - 4 - Non-Separable Data
12:55
3 - 1 - Learning with Different Output Space
17:26
3 - 2 - Learning with Different Data Label
18:12
3 - 3 - Learning with Different Protocol
11:09
3 - 4 - Learning with Different Input Space
14:13
4 - 1 - Learning is Impossible
13:32
4 - 2 - Probability to the Rescue
11:32
4 - 3 - Connection to Learning
16:46
4 - 4 - Connection to Real Learning
18:05
5 - 1 - Recap and Preview
13:44
5 - 2 - Effective Number of Lines
15:26
5 - 3 - Effective Number of Hypotheses
16:17
5 - 4 - Break Point
07:44
6 - 1 - Restriction of Break Point
14:18
6 - 2 - Bounding Function Basic Cases
06:56
6 - 3 - Bounding Function Inductive Cases
14:46
6 - 4 - A Pictorial Proof
16:01
7 - 1 - Definition of VC Dimension
13:10
7 - 2 - VC Dimension of Perceptrons
13:27
7 - 3 - Physical Intuition of VC Dimension
06:10
7 - 4 - Interpreting VC Dimension
17:13
8 - 1 - Noise and Probabilistic Target
17:01
8 - 2 - Error Measure
15:10
8 - 3 - Algorithmic Error Measure
13:47
8 - 4 - Weighted Classification (16-54)
16:55
9 - 1 - Linear Regression Problem
10:08
9 - 2 - Linear Regression Algorithm (20-03)
20:04
9 - 3 - Generalization Issue
20:34
9 - 4 - Linear Regression for Binary Classification
11:23
10 - 1 - Logistic Regression Problem
14:32
10 - 2 - Logistic Regression Error
15:58
10 - 3 - Gradient of Logistic Regression Error
15:37
10 - 4 - Gradient Descent
19:18
11 - 1 - Linear Models for Binary Classification
21:35
11 - 2 - Stochastic Gradient Descent
11:39
11 - 3 - Multiclass via Logistic Regression
14:18
11 - 4 - Multiclass via Binary Classification
11:35
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)
09:36
13 - 1 - What is Overfitting- (10-45)
10:45
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 - 1 - Regularized Hypothesis Set (19-16)
19:17
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 - 1 - Occam-'s Razor (10-08)
10:09
16 - 2 - Sampling Bias (11-50)
11:51
16 - 3 - Data Snooping (12-28)
12:29
16 - 4 - Power of Three (08-49)
08:50
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