Machine Learning
1. Linear Regression
2. Classification
3. Generative Learning Algorithms
4. Kernel Methods
5. Learning Theory
6. Clustering
7. Principal Component Analysis
8. Independent Component Analysis
9. Expectancy Maximization Algorithm
10. Gaussian Mixture Models
11. Factor Analysis
12. Variational Autoencoders
13. Decision Trees
14. Reinforcement Learning
Acknowledgments:
These notes are adapted from and meant to be a supplementary resource to the CS229 Machine Learning Notes at Stanford.
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