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16 Free eBooks On Machine Learning!
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16 Free eBooks On Machine Learning!  
Those involved in the field of Robotics and Artificial Intelligence are well aware of 'Machine Learning'. Here we bring to you 16 ebooks on the discipline, which are free to read and download!   
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Saturday, November 16, 2013 Are you interested in the intense, lucrative and adventurous field of Robotics and Artificial Intelligence? Well although they sounds astonishing, but there is indeed a lot of hard work required to excel in these streams. If you too are planning to kick start your career in one of these or if you are already studying and require some resources, here we bring some help with 16 absolutely free ebooks on Machine Learning! Make awesome robots!

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The LION Way: Machine Learning plus Intelligent Optimization
by Roberto Battiti, Mauro Brunato - Lionsolver, Inc., 2013
The introduction of the book says, “Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex problems. This book is about increasing the automation level and connecting data directly to decisions and actions.”

A Course in Machine Learning
by Hal Daumé III - ciml.info, 2012
The introduction of the book says, “This is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.”

A First Encounter with Machine Learning
by Max Welling - University of California Irvine, 2011
The introduction of the book says, “The book you see before you is meant for those starting out in the field of machine learning, who need a simple, intuitive explanation of some of the most useful algorithms that our field has to offer. A prelude to the more advanced text books.”

Bayesian Reasoning and Machine Learning
by David Barber - Cambridge University Press, 2011
The introduction of the book says, “The book is designed for final-year undergraduate students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basics to advanced techniques within the framework of graphical models.”

Introduction to Machine Learning
by Amnon Shashua - arXiv, 2009
The introduction of the book says, “Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).”

The Elements of Statistical Learning: Data Mining, Inference, and Prediction
by T. Hastie, R. Tibshirani, J. Friedman - Springer, 2009
The introduction of the book says, “This book brings together many of the important new ideas in learning, and explains them in a statistical framework. The authors emphasize the methods and their conceptual underpinnings rather than their theoretical properties.”

Reinforcement Learning
by C. Weber, M. Elshaw, N. M. Mayer - InTech, 2008
The introduction of the book says, “This book describes and extends the scope of reinforcement learning. It also shows that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional controllers.”

Machine Learning
by Abdelhamid Mellouk, Abdennacer Chebira - InTech, 2009
The introduction of the book says, “Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, and more.”

How Are We To Know?
by Nils J. Nilsson - Stanford University, 2006
The introduction of the book says, “This book is about beliefs -- how we get them and how we evaluate them. It takes the form of a fictional conversation among three people and Gio, an intelligent robot. The level of exposition is neither technical nor deeply philosophical.”

Reinforcement Learning: An Introduction
by Richard S. Sutton, Andrew G. Barto - The MIT Press, 1998
The introduction of the book says, “The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.”

Gaussian Processes for Machine Learning
by Carl E. Rasmussen, Christopher K. I. Williams - The MIT Press, 2005
The introduction of the book says, “Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.”

Machine Learning, Neural and Statistical Classification
by D. Michie, D. J. Spiegelhalter - Ellis Horwood, 1994
The introduction of the book says, “The book provides a review of different approaches to classification, compares their performance on challenging data-sets, and draws conclusions on their applicability to realistic industrial problems. A wide variety of approaches has been taken.”

Introduction To Machine Learning
by Nils J Nilsson, 1997
The introduction of the book says, “This book concentrates on the important ideas in machine learning, to give the reader sufficient preparation to make the extensive literature on machine learning accessible. The author surveys the important topics in machine learning circa 1996.”

Inductive Logic Programming: Techniques and Applications
by Nada Lavrac, Saso Dzeroski - Prentice Hall, 1994
The introduction of the book says, “This book is an introduction to inductive logic programming. It covers empirical inductive logic programming with applications in knowledge acquisition, inductive program synthesis, inductive data engineering, and knowledge discovery in databases.”

Practical Artificial Intelligence Programming in Java
by Mark Watson - Lulu.com, 2008
The introduction of the book says, “The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).”

Information Theory, Inference, and Learning Algorithms
by David J. C. MacKay - Cambridge University Press, 2003
The introduction of the book says, “A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.”

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