According to Andrew Ng notes at Stanford's "machine learning" video, does not go into details through knowledge Li Hang "statistical learning methods" to obtain the list only outline.
Supervised learning
- Linear regression https://www.cnblogs.com/angelica-duhurica/p/10912097.html
- Logistic regression https://www.cnblogs.com/angelica-duhurica/p/10912097.html
- Neural network https://www.cnblogs.com/angelica-duhurica/p/10928012.html
- SVM https://www.cnblogs.com/angelica-duhurica/p/10957870.html
Unsupervised Learning
- K-means https://www.cnblogs.com/angelica-duhurica/p/10958085.html
- PCA principal component analysis https://www.cnblogs.com/angelica-duhurica/p/10958085.html
- Anomaly detection https://www.cnblogs.com/angelica-duhurica/p/10962078.html
application
- Recommended system https://www.cnblogs.com/angelica-duhurica/p/10962311.html
- Large-scale machine learning https://www.cnblogs.com/angelica-duhurica/p/10963470.html
Suggest
- Deviation / variance https://www.cnblogs.com/angelica-duhurica/p/10948753.html
- Regularization https://www.cnblogs.com/angelica-duhurica/p/10912097.html
- What to do next: Assessing learning algorithms, learning curve, error analysis, the upper limit of analysis https://www.cnblogs.com/angelica-duhurica/p/10963474.html