# machine learning, # statistical learning, # econometrics, # nonparametric methods, # big data analysis, # inference, # prediction
Machine learning is a big wave of quantitative analysis. Definitely, worth efforts to extend your skills to this field.
Intuitive graphical understanding and algebraic rough sketches are way more important than mathematical details. Good understanding of algorithm processes, I believe, would make us devise a new hybrid process and focus more on practical analyses.
1. Machine Learning
2. Performance Measure
3. Statistical Tests for Machine Learning
4. Linear Regression
5. Logistic Regression
6. KNN
7. Spline and Generalized Additive Model(GAM)
8. Regularization: Ridge, LASSO, and Elastic Net
9. Tree Model
10. Bagging, Random Forest and Boosting
11. Discriminant Analysis
12. Support Vector Machine(SVM)
13. Principal Component Analysis(PCA)
14. K-Means Clustering
15. Hierarchical Clustering
Missing Values and Imputation
Automated Feature Selection
Outliers and High Leverage Points
Wrapper Function