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# NTU-HsuanTienLin-MachineLearning

## 课程介绍

**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。《机器学习技法》课程主要介绍了机器学习领域经典的一些算法，包括支持向量机、决策树、随机森林、神经网络等等。林老师的教学风格也很幽默风趣，总让读者在轻松愉快的氛围中掌握知识。在此，笔者将把这两门课的所有视频、笔记、书籍等详细资料分享给大家。

Hsuan-Tien Lin 机器学习基石

## 课程内容

### 《机器学习基石》

• When Can Machine Learn?

• Why Can Machine Learn?

• How Can Machine Learn?

• How Can Machine Learn Better?

• When Can Machine Learn?

• The Learning Problem

• Types of Learning

• Feasibility of Learning

• Why Can Machine Learn?

• Training versus Testing

• Theory of Generalization

• The VC Dimension

• Noise and Error

• How Can Machine Learn?

• Linear Regression

• Logistic Regression

• Logistic Regression

• Nonlinear Transformation

• How Can Machine Learn Better?

• Hazard of Overfitting

• Regularization

• Validation

• Three Learning Principles

### 《机器学习技法》

• Embedding Numerous Features: Kernel Models

• Combining Predictive Features: Aggregation Models

• Distilling Implicit Features: Extraction Models

• Embedding Numerous Features: Kernel Models

• Linear Support Vector Machine

• Dual Support Vector Machine

• Kernel Support Vector Machine

• Soft-Margin Support Vector Machine

• Kernel Logistic Regression

• Support Vector Regression

• Combining Predictive Features: Aggregation Models

• Blending and Bagging

• Decision Tree

• Random Forest

• Distilling Implicit Features: Extraction Models

• Neural Network

• Deep Learning

• Matrix Factorization

• Finale

## 资源汇总

### 课程书籍

Learning From Data