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.
