资源｜台大林轩田：机器学习基石（全套65课中文视频）

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台湾大学台大林轩田老师的全套《机器学习基石》教程用中文讲解，赶紧收藏：

1.1 The Learning Problem - Course Introduction

https://www.bilibili.com/video/av12463015/#page=1

1.2 The Learning Problem - What Is Machine Learning

https://www.bilibili.com/video/av12463015/#page=2

1.3 The Learning Problem - Applications of Machine Learning

https://www.bilibili.com/video/av12463015/#page=3

1.4 The Learning Problem - Components of Learning

https://www.bilibili.com/video/av12463015/#page=4

1.5 The Learning Problem - Machine Learning and Other Fields

https://www.bilibili.com/video/av12463015/#page=5

2.1 Learning to Answer Yes_No - Perceptron Hypothesis Set

https://www.bilibili.com/video/av12463015/#page=6

2.2 Learning to Answer Yes_No - Perceptron Learning Algorithm

https://www.bilibili.com/video/av12463015/#page=7

2.3 Learning to Answer Yes_No - Guarantee of PLA

https://www.bilibili.com/video/av12463015/#page=8

2.4 Learning to Answer Yes_No - Non-Separable Data

https://www.bilibili.com/video/av12463015/#page=9

3.1 Types of Learning - Learning with Different Output Space

https://www.bilibili.com/video/av12463015/#page=20

3.2 Types of Learning - Learning with Different Data Label

https://www.bilibili.com/video/av12463015/#page=11

3.3 Types of Learning - Learning with Different Protocol

https://www.bilibili.com/video/av12463015/#page=12

3.4 Types of Learning - Learning with Different Input Space

https://www.bilibili.com/video/av12463015/#page=13

4.1 Feasibility of Learning - Learning is Impossible

https://www.bilibili.com/video/av12463015/#page=14

4.2 Feasibility of Learning - Probability to the Rescue

https://www.bilibili.com/video/av12463015/#page=15

4.3 Feasibility of Learning - Connection to Learning

https://www.bilibili.com/video/av12463015/#page=16

4.4 Feasibility of Learning - Connection to Real Learning

https://www.bilibili.com/video/av12463015/#page=17

5.1 Training versus Testing - Recap and Preview

https://www.bilibili.com/video/av12463015/#page=18

5.2 Training versus Testing - Effective Number of Lines

https://www.bilibili.com/video/av12463015/#page=19

5.3 Training versus Testing - Effective Number of Hypotheses

https://www.bilibili.com/video/av12463015/#page=20

5.4 Training versus Testing - Break Point

https://www.bilibili.com/video/av12463015/#page=21

6.1 Theory of Generalization - Restriction of Break Point

https://www.bilibili.com/video/av12463015/#page=22

6.2 Theory of Generalization - Bounding Function - Basic Cases

https://www.bilibili.com/video/av12463015/#page=23

6.3 Theory of Generalization - Bounding Function - Inductive

https://www.bilibili.com/video/av12463015/#page=24

6.4 Theory of Generalization - A Pictorial Proof

https://www.bilibili.com/video/av12463015/#page=25

7.1 The VC Dimension - Definition of VC Dimension

https://www.bilibili.com/video/av12463015/#page=26

7.2 The VC Dimension - VC Dimension of Perceptrons

https://www.bilibili.com/video/av12463015/#page=27

7.3 The VC Dimension - Physical Intuition of VC Dimension

https://www.bilibili.com/video/av12463015/#page=28

7.4 The VC Dimension - Interpreting VC Dimension

https://www.bilibili.com/video/av12463015/#page=29

8.1 Noise and Error - Noise and Probabilistic Target

https://www.bilibili.com/video/av12463015/#page=20

8.2 Noise and Error - Error Measure

https://www.bilibili.com/video/av12463015/#page=31

8.3 Noise and Error - Algorithmic Error Measure

https://www.bilibili.com/video/av12463015/#page=32

8.4 Noise and Error - Weighted Classification

https://www.bilibili.com/video/av12463015/#page=33

9.1 Linear Regression - Linear Regression Problem

https://www.bilibili.com/video/av12463015/#page=34

9.2 Linear Regression - Linear Regression Algorithm

https://www.bilibili.com/video/av12463015/#page=35

9.3 Linear Regression - Generalization Issue

https://www.bilibili.com/video/av12463015/#page=36

9.4 Linear Regression - for Binary Classification

https://www.bilibili.com/video/av12463015/#page=37

10.1 Logistic Regression - Logistic Regression Problem

https://www.bilibili.com/video/av12463015/#page=38

10.2 Logistic Regression - Logistic Regression Error

https://www.bilibili.com/video/av12463015/#page=39

10.3 Logistic Regression - Gradient of Logistic Regression Error

https://www.bilibili.com/video/av12463015/#page=40

10.4 Logistic Regression - Gradient Descent

https://www.bilibili.com/video/av12463015/#page=41

11.1 Linear Models for Classification - Binary Classification

https://www.bilibili.com/video/av12463015/#page=42

11.2 Linear Models for Classification - Stochastic Grad. Descent

https://www.bilibili.com/video/av12463015/#page=43

11.3 Linear Models for Classification - Multiclass via Logistic

https://www.bilibili.com/video/av12463015/#page=44

11.4 Linear Models for Classification - Multiclass via Binary

https://www.bilibili.com/video/av12463015/#page=45

12.1 Nonlinear Transformation - Quadratic Hypotheses

https://www.bilibili.com/video/av12463015/#page=46

12.2 Nonlinear Transformation - Nonlinear Transform

https://www.bilibili.com/video/av12463015/#page=47

12.3 Nonlinear Transformation - Price of Nonlinear Transform

https://www.bilibili.com/video/av12463015/#page=48

12.4 Nonlinear Transformation - Structured Hypothesis Sets

https://www.bilibili.com/video/av12463015/#page=49

13.1 Hazard of Overfitting - What is Overfitting

https://www.bilibili.com/video/av12463015/#page=50

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

https://www.bilibili.com/video/av12463015/#page=51

13.3 Hazard of Overfitting - Deterministic Noise

https://www.bilibili.com/video/av12463015/#page=52

13.4 Hazard of Overfitting - Dealing with Overfitting

https://www.bilibili.com/video/av12463015/#page=53

14.1 Regularization - Regularized Hypothesis Set

https://www.bilibili.com/video/av12463015/#page=54

14.2 Regularization - Weight Decay Regularization

https://www.bilibili.com/video/av12463015/#page=55

14.3 Regularization - Regularization and VC Theory

https://www.bilibili.com/video/av12463015/#page=56

14.4 Regularization - General Regularizers

https://www.bilibili.com/video/av12463015/#page=57

15.1 Validation - Model Selection Problem

https://www.bilibili.com/video/av12463015/#page=58

15.2 Validation - Validation

https://www.bilibili.com/video/av12463015/#page=59

15.3 Validation - Leave-One-Out Cross Validation

https://www.bilibili.com/video/av12463015/#page=60

15.4 Validation - V-Fold Cross Validation

https://www.bilibili.com/video/av12463015/#page=61

16.1 Three Learning Principles - Occam's Razor

https://www.bilibili.com/video/av12463015/#page=62

16.2 Three Learning Principles - Sampling Bias

https://www.bilibili.com/video/av12463015/#page=63

16.3 Three Learning Principles - Data Snooping

https://www.bilibili.com/video/av12463015/#page=64

16.4 Three Learning Principles - Power of Three

https://www.bilibili.com/video/av12463015/#page=65

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