Machine Learning, etc

I needed to refresh some Graphical Models basics, and here are lecture notes I found useful

• Jeff Bilmes' Graphical Models course , gives detailed introduction on Lauritzen's results, proof of Hammersley-Clifford results (look in "old scribes" section for notes)
• Kevin Murphy's Graphical Models course , good description of I-maps
• Sam Roweis' Graphical Models course , good introduction on exponential families
• Stephen Wainwright's Graphical Models class, exponential families, variational derivation of inference
• Michael Jordan's Graphical Models course, his book seems to be the most popular for this type of class
  
• Lauritzen's Graphical Models and Inference class , also his Statistical Inference class , useful facts on exponential families, information on log-linear models, decomposability
• Donna Precup's class , some stuff on structure learning

Additionally, for information on directed/undirected graph equivalence classes, this paper by Meek is good, also this thesis gives some information for equivalence classes in ancestral/chain graphs