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林轩田机器学习基石(国语)

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2017-07-22 16:15:09

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https://www.youtube.com/playlist?list=PLXVfgk9fNX2I7tB6oIINGBmW50rrmFTqf Machine Learning Foundations (機器學習基石)

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1.1 The Learning Problem - Course Introduction

09:50

1.2 The Learning Problem - What Is Machine Learning

18:30

1.3 The Learning Problem - Applications of Machine Learning

18:58

1.4 The Learning Problem - Components of Learning

11:46

1.5 The Learning Problem - Machine Learning and Other Fields

10:22

2.1 Learning to Answer Yes_No - Perceptron Hypothesis Set

15:12

2.2 Learning to Answer Yes_No - Perceptron Learning Algorithm

19:47

2.3 Learning to Answer Yes_No - Guarantee of PLA

12:38

2.4 Learning to Answer Yes_No - Non-Separable Data

12:56

3.1 Types of Learning - Learning with Different Output Space

16:57

3.2 Types of Learning - Learning with Different Data Label

18:14

3.3 Types of Learning - Learning with Different Protocol

11:10

3.4 Types of Learning - Learning with Different Input Space

14:14

4.1 Feasibility of Learning - Learning is Impossible

13:03

4.2 Feasibility of Learning - Probability to the Rescue

11:33

4.3 Feasibility of Learning - Connection to Learning

16:47

4.4 Feasibility of Learning - Connection to Real Learning

18:07

5.1 Training versus Testing - Recap and Preview

13:19

5.2 Training versus Testing - Effective Number of Lines

15:27

5.3 Training versus Testing - Effective Number of Hypotheses

16:18

5.4 Training versus Testing - Break Point

07:44

6.1 Theory of Generalization - Restriction of Break Point

13:47

6.2 Theory of Generalization - Bounding Function - Basic Cases

06:57

6.3 Theory of Generalization - Bounding Function - Inductive

14:48

6.4 Theory of Generalization - A Pictorial Proof

16:02

7.1 The VC Dimension - Definition of VC Dimension

12:34

7.2 The VC Dimension - VC Dimension of Perceptrons

13:28

7.3 The VC Dimension - Physical Intuition of VC Dimension

06:11

7.4 The VC Dimension - Interpreting VC Dimension

17:14

8.1 Noise and Error - Noise and Probabilistic Target

16:30

8.2 Noise and Error - Error Measure

15:11

8.3 Noise and Error - Algorithmic Error Measure

13:48

8.4 Noise and Error - Weighted Classification

16:55

9.1 Linear Regression - Linear Regression Problem

09:37

9.2 Linear Regression - Linear Regression Algorithm

20:04

9.3 Linear Regression - Generalization Issue

20:35

9.4 Linear Regression - for Binary Classification

11:24

10.1 Logistic Regression - Logistic Regression Problem

14:02

10.2 Logistic Regression - Logistic Regression Error

15:59

10.3 Logistic Regression - Gradient of Logistic Regression Error

15:39

10.4 Logistic Regression - Gradient Descent

19:19

11.1 Linear Models for Classification - Binary Classification

20:59

11.2 Linear Models for Classification - Stochastic Grad. Descent

11:40

11.3 Linear Models for Classification - Multiclass via Logistic

14:19

11.4 Linear Models for Classification - Multiclass via Binary

11:36

12.1 Nonlinear Transformation - Quadratic Hypotheses

23:21

12.2 Nonlinear Transformation - Nonlinear Transform

09:53

12.3 Nonlinear Transformation - Price of Nonlinear Transform

15:38

12.4 Nonlinear Transformation - Structured Hypothesis Sets

09:37

13.1 Hazard of Overfitting - What is Overfitting

10:19

13.2 Hazard of Overfitting - The Role of Noise and Data Size

13:36

13.3 Hazard of Overfitting - Deterministic Noise

14:08

13.4 Hazard of Overfitting - Dealing with Overfitting

10:50

14.1 Regularization - Regularized Hypothesis Set

18:45

14.2 Regularization - Weight Decay Regularization

24:09

14.3 Regularization - Regularization and VC Theory

08:16

14.4 Regularization - General Regularizers

13:29

15.1 Validation - Model Selection Problem

15:34

15.2 Validation - Validation

13:25

15.3 Validation - Leave-One-Out Cross Validation

16:07

15.4 Validation - V-Fold Cross Validation

10:42

16.1 Three Learning Principles - Occam's Razor

09:37

16.2 Three Learning Principles - Sampling Bias

11:51

16.3 Three Learning Principles - Data Snooping

12:29

16.4 Three Learning Principles - Power of Three

08:50

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