1. Supervised learning
- 1.2. Linear and Quadratic Discriminant Analysis
- 1.3. Kernel ridge regression
- 1.5. Stochastic Gradient Descent
- 1.6. Nearest Neighbors
- 1.7. Gaussian Processes
- 1.8. Cross decomposition
- 1.10. Decision Trees
- 1.13. Feature selection
- 1.14. Semi-Supervised
- 1.15. Isotonic regression
- 1.17. Neural network models (supervised)