If designing and programming the brain for a robot fantasize you, then ‘Machine Learning’ is your subject. Here we bring to you 15 ebooks on the discipline, which are free to read and download!
Are you an adventurous geek who always wanted to study Robotics and Artificial Intelligence? So for those planning to kick start their career in these fields or those already studying who are hunting for some resources, here we bring some help with 15 absolutely free ebooks on Machine Learning! Make awesome robots!
1. A Course in Machine Learning
Author/s: Hal Daumé III
Publisher: 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.”
2. A First Encounter with Machine Learning
Author/s: Max Welling
Publisher: 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.”
3. Bayesian Reasoning and Machine Learning
Author/s: David Barber
Publisher: 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.”
4. Introduction to Machine Learning
Author/s: Amnon Shashua
Publisher: 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).”
5. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Author/s: 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.”
Author/s: C. Weber, M. Elshaw, N. M. Mayer
Publisher: 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.”
Author/s: Abdelhamid Mellouk, Abdennacer Chebira
Publisher: 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.”
8. Reinforcement Learning: An Introduction
Author/s: Richard S. Sutton, Andrew G. Barto
Publisher: 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.”
9. Machine Learning, Neural and Statistical Classification
Author/s: D. Michie, D. J. Spiegelhalter
Publisher: 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.”
10. Introduction To Machine Learning
Author/s: 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.”
11. Practical Artificial Intelligence Programming in Java
Author/s: Mark Watson
Publisher: 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).”
12. Information Theory, Inference, and Learning Algorithms
Author/s: David J. C. MacKay
Publisher: 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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The writer is a senior correspondent at EFY, Gurgaon
