Applications of Reinforcement Learning (Stanford)

Lecture 1 The Motivation & Applications of Machine Learning

Lecture 2 An Application of Supervised Learning - Autonomous Deriving

Lecture 3 The Concept of Underfitting and Overfitting

Lecture 4 Newton's Method

Lecture 5 Discriminative Algorithms

Lecture 6 Multinomial Event Model

Lecture 7 Optimal Margin Classifier

Lecture 8 Kernels

Lecture 9 Bias/variance Tradeoff

Lecture 10 Uniform Convergence - The Case of Infinite H

Lecture 11 Bayesian Statistics and Regularization

Lecture 12 The Concept of Unsupervised Learning

Lecture 13 Mixture of Gaussian

Lecture 14 The Factor Analysis Model

Lecture 15 Latent Semantic Indexing (LSI)

Lecture 16 Applications of Reinforcement Learning

Lecture 17 Generalization to Continuous States

Lecture 18 State-action Rewards

Lecture 19 Advice for Applying Machine Learning

Lecture 20 Partially Observable MDPs (POMDPs)