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Machine Learning

Reinforcement Learning:An Introduction

course on machine learning from the Stanford university:

http://www.stanford.edu/class/cs229/

Introduction:

Basic concepts.

Supervised learning:

Supervised learning setup. LMS.

Logistic regression. Perceptron. Exponential family.

Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes.

Support vector machines.

Model selection and feature selection.

Ensemble methods: Bagging, boosting, ECOC.

Evaluating and debugging learning algorithms.

Learning theory:

Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds.

VC dimension. Worst case (online) learning.

Practical advice on how to use learning algorithms.

Unsupervised learning:

Clustering. K-means.

EM. Mixture of Gaussians.

Factor analysis.

PCA. MDS. pPCA.

Independent components analysis (ICA).

Reinforcement learning and control:

MDPs. Bellman equations.

Value iteration and policy iteration.

Linear quadratic regulation (LQR). LQG.

Q-learning. Value function approximation.
Policy search. Reinforce. POMDPs.