Machine Learning

1. introduction
2. linear-regression-and-gradient-descent
3. locally-weighted-and-logicistic-regression
4. perceptron–generalized-linear-model
5. gda–naive-bayes
6. support-vector-machines
7. kernels
8. data-splits-models–cross-validation
9. approximation-estimation-error–erm
10. decision-trees-and-ensemble-methods
11. introduction-to-neural-networks
12. back-propagation-and-improving-neural-networks
13. debugging-ml-models-and-error-analysis
14. expectation-maximization-algorithms
15. em-algorithm–factor-analysis
16. independent-component-analysis-and-reinforcement-learning
17. mdps–valuepolicy-iteration
18. mdps–model-simulation
19. reward-model-and-linear-dynamical-system
20. debugging-and-diagnostics