diff --git a/free-programming-books.md b/free-programming-books.md
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@@ -307,23 +307,23 @@
 * [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