[Resources] Textbooks for the 2012 classes?

The NLP reccomended textbooks:

are mentioned in the description of course in this post. Some of them are completely free, so no harm in getting them.

• Introduction to Information Retrieval - Manning, Schütze and Raghavan 2008 (free);
• Natural Language Processing with Python - Bird, Klein and Loper 2009 (free).

Other Free online books (for ML and PGM):

• Computer Vision: Models, Learning, and Inference (Simon J.D. Prince) - A free online version of upcoming book. There is also number of additional resources that complement the data in each chapter. These include links to project pages, other descriptions of the same material and useful datasets.
• The Elements of Statistical Machine Learning (Hastie, Tibshirani and Friedman (2008)). It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book.
• Reinforcement Learning: An Introduction (Richard S. Sutton and Andrew G. Barto). This book was designed to be used as a text in a one-semester course, perhaps supplemented by readings from the literature or by a more mathematical text such as the excellent one by Bertsekas and Tsitsiklis (1996). This book can also be used as part of a broader course on machine learning, artificial intelligence, or neural networks.
• Introduction to Machine Learning (Alex Smola and S.V.N. Vishwanathan (2008)). Check out his blog and PURDUE Machine Learning Summer School 2011 video lectures.
• Bayesian Reasoning and Machine Learning (Barber, D.). The book is designed to appeal to students with only a modest mathematical background in undergraduate calculus and linear algebra. No formal computer science or statistical background is required to follow the book, although a basic familiarity with probability, calculus and linear algebra would be useful. The book should appeal to students from a variety of backgrounds, including Computer Science, Engineering, applied Statistics, Physics, and Bioinformatics that wish to gain an entry to probabilistic approaches in Machine Learning.
• Graphical Models, Exponential Families, and Variational Inference (Martin J. Wainwright and Michael I. Jordan (2008)). We describe how a wide variety of algorithms — among them sum-product, cluster variational methods, expectation-propagation, mean field methods, max-product and linear programming relaxation, as well as conic programming relaxations — can all be understood in terms of exact or approximate forms of these variational representations.

I bought AIMA and thought it helpful for the additional depth it provided. I especially liked the historical notes and detailed bibliography.

For ML I did not use a textbook (except for AIMA where applicable). I looked around a bit, but did not find one I thought would add enough value to buy. One that tempted me was Pattern Recognition and Machine Learning. That might be worth considering if you really like learning from books.

I purchased the optional text for PGM: Probabilistic Graphical Models: Principles and Techniques. After reading the introductory material (chapters 1 and 2 and appendix A) I'm glad I did (it looks like a good book). I ran across some online books that might be worth checking out as well. This book is recommended in PGM appendix A for more detail on optimization if desired: http://www.stanford.edu/~boyd/cvxbook/ This book has been recommended in a few aiqus threads: http://www.inference.phy.cam.ac.uk/mackay/itprnn/book.html

I purchased a copy of the first edition of Jurafsky and Martin for NLP. I'm hoping that will be sufficient. http://nlp.stanford.edu/IR-book/ (by Chris Manning, the other instructor) might be worth a look as well.

I also bought Peter Norvig's AIMA and have hardly used it during the course. I mean, I began using it extensively in the first lessons but after that, job, family and those kind of things forced me to only watch the videos and very few times (midterm Q1 about agents and environments, noise and Laplace smoothing and so) go to the book.

Now I'm going to take the Probabilistic Graphical Models course and I haven't planned to buy any book.

No textbook for Machine Learning.

PGM -

For additional depth, you can refer to the best-selling textbook, "Probabilistic Graphical Models: Principles and Techniques" by Daphne and Nir Friedman.

NLP -

The best textbook for the class is Jurafsky and Martin, Speech and Language Processing 2nd Edition, complemented by chapters from Introduction to Information Retrieval - Manning, Schütze and Raghavan 2008 (free); other useful, good books include Manning and Schütze 1999 (2 chapters free), and Natural Language Processing with Python - Bird, Klein and Loper 2009 (free).

quoted from class pages (see right-hand menu)