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。

首先附上这两门课的主页：

Hsuan-Tien Lin 机器学习基石

课程视频在B站上可以直接观看哦～这里附上传送门：

机器学习基石（林轩田）

机器学习技法（林轩田）

课程内容

《机器学习基石》

这门课主要涉及机器学习关键问题的四个方面：

When Can Machine Learn?
Why Can Machine Learn?
How Can Machine Learn?
How Can Machine Learn Better?

其中每个方面包含4节课，总共有16节课。具体所有课程内容如下：

When Can Machine Learn?
- The Learning Problem
- Learning to Answer Yes/No
- 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》

这门课主要涉及机器学习经典算法的三个方面：

Embedding Numerous Features: Kernel Models
Combining Predictive Features: Aggregation Models
Distilling Implicit Features: Extraction Models

总共有16节课。具体所有课程内容如下：

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
- Adaptive Boosting
- Decision Tree
- Random Forest
- Gradient Boosted Decision Tree
Distilling Implicit Features: Extraction Models
- Neural Network
- Deep Learning
- Radial Basis Function Network
- Matrix Factorization
- Finale