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

Note: I wrote these notes for my own reference while I was taking the CS229 Machine Learning course at Stanford offered by Anand Avati in Summer 2020. I hope they are useful for others.

Please use a larger screen

This content is best viewed on a laptop or desktop device.