Machine Learning

Content

1. Bayesian Decision Theory
2. Maximum Likelihood and Bayes Parameter Estimation
3. Principal Component Analysis
4. Fisher Linear Discriminant
5. Model Selection
6. Learning Theory and Kernels
7. Support Vector Machines
8. Boosting
9. Decision Trees and Random Forests
10. Kernel Ridge Regression
11. Neural Networks
12. Latent Variable Models
13. Products of Experts
14. Explainable AI