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* [AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java](http://wps.aw.com/wps/media/objects/5771/5909832/PDF/Luger_0136070477_1.pdf) - George F. Luger, William A Stubblefield
* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
* [Artificial Intelligence | Machine Learning](http://see.stanford.edu/see/materials/aimlcs229/handouts.aspx) - Andrew Ng *(Notes, lectures, and problems)*
* [Artificial Intelligence A Modern Approach](http://51lica.com/wp-content/uploads/2012/05/Artificial-Intelligence-A-Modern-Approach-3rd-Edition.pdf) (PDF)
* [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage)
* [Computer Vision: Algorithms and Applications](http://hackershelf.com/book/134/computer-vision-algorithms-and-applications/)
* [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/)
* [Inductive Logic Programming](http://www-ai.ijs.si/SasoDzeroski/ILPBook/)
* [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/)
* [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf) (PDF)
* [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf) - Alex Smola and S.V.N. Vishwanathan (PDF)
* [Introduction to Machine Learning](http://arxiv.org/abs/0904.3664v1) - Amnon Shashua
* [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf) (PDF)
* [Machine Learning](http://www.intechopen.com/books/machine_learning)
* [Machine Learning, Neural and Statistical Classification](http://www1.maths.leeds.ac.uk/~charles/statlog/whole.pdf) (PDF) or [online version](http://www1.maths.leeds.ac.uk/~charles/statlog/) - This book is based on the EC (ESPRIT) project StatLog.
* [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)
* [Probabilistic Models in the Study of Language](http://idiom.ucsd.edu/~rlevy/pmsl_textbook/text.html) (Draft, with R code)
* [Reinforcement Learning: An Introduction](http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html)
* [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
* [The Python Game Book](http://thepythongamebook.com/en:start)
* [The LION Way: Machine Learning plus Intelligent Optimization](http://www.e-booksdirectory.com/details.php?ebook=9575)
* [Introduction to Machine Learning](http://arxiv.org/abs/0904.3664v1)
* [Machine Learning](http://www.intechopen.com/books/machine_learning)
* [Inductive Logic Programming](http://www-ai.ijs.si/SasoDzeroski/ILPBook/)
* [Artificial Intelligence A Modern Approach](http://51lica.com/wp-content/uploads/2012/05/Artificial-Intelligence-A-Modern-Approach-3rd-Edition.pdf) (PDF)
* [The Python Game Book](http://thepythongamebook.com/en:start)
####Mathematics
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###Ada
* [A Guide to Ada for C and C++ Programmers](http://www.cs.uni.edu/~mccormic/4740/guide-c2ada.pdf) (PDF)
* [Ada 95: The Craft of Object-Oriented Programming](http://faculty.cs.wwu.edu/reedyc/AdaResources/bookhtml/contents.htm)
* [Ada Distilled](http://www.adapower.com/pdfs/AdaDistilled07-27-2003.pdf) (PDF)
* [Ada for Software Engineers](http://pnyf.inf.elte.hu/kto/oktatas/ada/books/ase.pdf) (PDF)
* [The Big Online Book of Linux Ada Programming](http://www.pegasoft.ca/resources/boblap/book.html)
* [A Guide to Ada for C and C++ Programmers](http://www.cs.uni.edu/~mccormic/4740/guide-c2ada.pdf) (PDF)
###Agda
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###Assembly Language
* [An introduction to reverse engineering for beginners](https://github.com/dennis714/RE-for-beginners)
* [Assembly Language Succinctly](http://www.syncfusion.com/Content/downloads/ebook/Assembly_Language_Succinctly.pdf)
* [Paul Carter's Tutorial on x86 Assembly](http://drpaulcarter.com/pcasm/)
* [PC Assembly Language](http://drpaulcarter.com/pcasm/) - Paul A. Carter
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* [The Second Book Of Machine Language](http://www.atariarchives.org/2bml/)
* [Wizard Code](http://vendu.twodots.nl/wizardcode.html)
* [x86 Assembly](http://en.wikibooks.org/wiki/X86_Assembly)
* [An introduction to reverse engineering for beginners](https://github.com/dennis714/RE-for-beginners)
####Non-X86