Provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Course contents
# 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.

Courtsey;Standford university