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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
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