The following references
will be useful is learning more about the techniques discussed in class
- An Introduction to Graphical Models can be downloaded from /net/mash1/book
- A tutorial on Hidden Markov Models (HMMs) by L. Rabiner
- A presentation on Introduction to Graphical Models by M. Jordan
- A tutorial on EM Algorithm by F.
Dellaert and another EM tutorial by
J. Bilmes
- A tutorial on log-linear models by N. Smith
- A introduction to Conditional Random Fields (CRFs) by C. Sutton and A. McCullum
- A survey of semi-supervised learning methods X. Zhu
- Belief Networks, Hidden Markov Models, and Markov Random Fields: A Unifying View
- Read chapters 1, 2, 8, 9, and 13 of Pattern Recognition and Machine Learning book by C. M. Bishop