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01 - Stanford CS229: Machine Learning Course

02 - Linear Regression & Gradient Descent

03 - Locally Weighted & Logistic Regression

04 - Perceptron & Generalized Linear Model

05 - GDA & Naive Bayes

06 - Support Vector Machines

07 - Kernels

08 - Data Splits, Models & Cross-Validation

09 - Approx/Estimation Error & ERM

10 - Decision Trees & Ensemble Methods

11 - Introduction to Neural Networks

12 - Backprop & Improving Neural Networks

13 - Debugging ML Models & Error Analysis

14 - Expectation-Maximization Algorithms

15 - EM Algorithm & Factor Analysis

16 - Independent Component Analysis & RL

17 - MDPs & Value/Policy Iteration

18 - Continuous State MDP & Model Simulation

19 - Reward Model and Linear Dynamical Systems

20 - RL Debugging and Diagnostics