753 karma · joined March 11, 2011
For those I'd suggest "Pattern Recognition and Machine Learning" by Bishop. I've read throughout this and it's really well organized and thought out. For more mathematically advanced ML stuff I'd suggest "Foundations of Machine Learning" by Mohri. For a good reference for anything else I'd suggest "Machine Learning: A Probabilistic Perspective" by Murphy. For more depth on graphical models look at "Probabilistic Graphical Models: Principles and Techniques" by Koller.
On the NLP front there's the standard texts "Speech and Language Processing" by Jurafsky and "Foundations of Statistical Natural Language Processing" by Manning.
I also like "An Introduction to Statistical Learning" by James, Witten, Hastie and Tibshirani.
I have visualized this "Brick-Wall" as a graph with data from Wikipedia pdf: http://dl.dropbox.com/u/3215373/Thermal-Brick-Wall.pdf