Machine Learning, Artificial Intelligence, Mathematics eBooks

This compilation contains all the best and commonly recommended for studying machine learning, artificial intelligence and related mathematical areas on most levels from novice to math wizard.

If you find any book particularly useful, please consider buying it!
It might take a while to upload this one so be patient and try to keep seeding :)

List of books is on pastebin because it's too long: http://pastebin.ca/2250000
(TPB)

*** machine learning ***

A Probabilistic Theory of Pattern Recognition (Devroye, 1996)
Bayesian Reasoning and Machine Learning (Barber, 2012)
Data Analysis with Open Source Tools (Janert, 2010)
Data Mining: Concepts and Techniques, 3rd Edition (Han, 2011)
Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition (Witten, 2011)
Doing Bayesian Data Analysis: A Tutorial with R and BUGS (Kruschke, 2010)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition (Hastie, 2008)
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions (Seni, 2010)
Gaussian Processes for Machine Learning (Rasmussen, 2005)
Information Theory, Inference and Learning Algorithms (MacKay, 2003)
Introduction to Information Retrieval (Manning, 2008)
Introduction to Machine Learning (Alpaydin, 2009)
Introduction to Machine Learning (Smola, 2010)
Introduction to Pattern Recognition: A Matlab Approach (Theodoridis, 2010)
Kernel Methods for Pattern Analysis (Shawe-Taylor, 2004)
Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Efron, 2010)
Learning Deep Architectures for AI (Bengio, 2009)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Scholkopf, 2001)
Machine Learning: An Algorithmic Perspective (Marsland, 2009)
Machine Learning (Mitchell, 1997)
Machine Learning for Hackers (Conway, 2012)
Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites (Russell, 2011)
Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition (Samarasinghe, 2006)
Pattern Recognition, 4th Edition (Theodoridis, 2008)
Pattern Recognition and Machine Learning (Bishop, 2007)
Probabilistic Graphical Models: Principles and Techniques (Koller, 2009)
Programming Collective Intelligence: Building Smart Web 2.0 Applications (Segaran, 2007)
Statistical Learning Theory (Vapnik, 1998)


*** artificial intelligence ***

Artificial General Intelligence (Goertzel, 2007)
Artificial Intelligence: A Modern Approach, 3rd Edition (Russell and Norvig, 2009)
Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th Edition (Luger, 2008)
Artificial Intelligence for Games, 2nd Edition (Millington, 2009)
Knowledge Representation and Reasoning (Brachman, 2004)
Prediction, Learning, and Games (Cesa-Bianchi, 2006)
Reinforcement Learning: An Introduction (Sutton and Barto, 1998)
The Quest for AI: A History of Ideas and Achievements (Nilsson, 2010)
Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability (Hutter, 2005)


*** evolution & biology inspired ***

A Field Guide to Genetic Programming (Poli, 2008)
Ant Colony Optimization (Dorigo, 2004)
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (Floreano, 2008)
Evolving Connectionist Systems: The Knowledge Engineering Approach (Kasabov, 2007)
Introduction to Evolutionary Computing (Eiben, 2008)
Swarm Intelligence (Eberhart, 2001)
The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music (Romero, 2007)


*** mathematics ***

****** analysis ***
Calculus, 3rd edition (Spivak, 1994)
Calculus (Strang, 1991)
Introduction to Analysis (Rosenlicht, 1985)
Mathematical Analysis, 2nd Edition (Apostol, 1974)

****** linear algebra ***
Fundamentals of Matrix Computations, 2nd Edition (Watkins, 2002)
Introduction to Linear Algebra, 4th Edition (Strang, 2009)
Linear Algebra (Shilov, 1977)
Matrix Algebra: Theory, Computations, and Applications in Statistics (Gentle, 2007)
Matrix Computations, 3rd Edition (Golub, 1996)

****** optimization ***
Convex Optimization (Boyd, 2004)
Introduction to Linear Optimization (Bertsimas, 1997)
Introduction to Stochastic Search and Optimization (Spall, 2003)
Numerical Optimization (Nocedal, 2006)
Optimization for Machine Learning (Sra, 2011)
Practical Methods of Optimization (Fletcher, 2000)

****** probability ***
A Course in Probability Theory, 3rd Edition (Chung, 2000)
First Look at Rigorous Probability Theory (Rosenthal, 2006)
An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd Edition (Feller, 1968)
An Introduction to Probability Theory and Its Applications, Vol. 2 (Feller, 1971)
Introduction to Probability, 2nd Edition (Bertsekas, 2008)
Introduction to Probability (Grinstead, 2006)
Probability Theory: The Logic of Science (Jaynes, 2003)
Schaum's Outline of Probability, Random Variables, and Random Processes (Hsu, 1996)

****** statistics ***
All of Nonparametric Statistics (Wasserman, 2010)
All of Statistics: A Concise Course in Statistical Inference (Wasserman, 2003)
An Introduction to Generalized Linear Models, 2nd Edition (Dobson, 2001)
Applied Linear Statistical Models, 5th Edition (Kutner, 2004)
Introduction to Bayesian Statistics, 2nd Edition (Bolstad, 2007)
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (Izenman, 2008)
Multivariate Data Analysis, 7th Edition (Hair, 2009)
Robust Statistics, 2nd Edition (Huber, 2009)
Statistics: The Exploration & Analysis of Data, 7th Edition (Peck and Devore, 2007)
Statistics in Plain English, 3rd Edition (Urdan, 2010)


*** other ***

Foundations of Statistical Natural Language Processing (Manning, 1999)
Gödel, Escher, Bach: An Eternal Golden Braid (Hofstadter, 1999)
How to Prove It: A Structured Approach (Velleman, 2006)
Probabilistic Robotics (Thrun, 2005)
Speech and Language Processing, 2nd Edition (Jurafsky, 2008)
The Algorithm Design Manual, 2nd Edition (Skiena, 2010)