Clustering

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1. 

   Mixture of Gaussian

   Stanford / Engineering
   Andrew Ng
   

   Mixture of Gaussian, Mixture of Naive Bayes - Text clustering (EM Application), Factor Analysis, Restrictions on a Covariance Matrix, The Factor Analysis Model, EM for Factor Analysis

2. 

   The Concept of Unsupervised Learning

   Stanford / Computer Science
   Andrew Ng
   

   The Concept of Unsupervised Learning, K-means Clustering Algorithm, K-means Algorithm, Mixtures of Gaussians and the EM Algorithm, Jensen's Inequality, The EM Algorithm, Summary

3. 

   Machine Learning

   Stanford / Computer Science
   Andrew Ng
   

   This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins);