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Top 10 Best Free eBooks for Machine Learning


http://devzum.com/2014/12/05/top-10-best-free-ebooks-for-machine-learning/

bart simpson books

If you are an adventurous geek who always desires to learn more about Artificial Intelligence and Robotics, then you must start planning to pursue your career in designing and programming the brains of robots. Though it may sound amazing at times, but it requires lots of hard work and effort to excel in this subject. So, if you are planning to pursue this career or already studying and searching for some awesome resources then below you will come across with few amazing eBooks on Machine Learning that will help you to design and program the brain of robots.

1) A Course in Machine Learning

The author, Hal Daumé III, started the introduction of this book with a set of introductory materials that provide lessons on modern machine learning including, probabilistic modeling, large margin methods, unsupervised or supervised learning etc. The prime focus of this book is on broad applications with a strong support or backbone.

2) The LION Way: Machine Learning plus Intelligent Optimization

The author of this free eBook on Machine Learning, Roberto Battiti & Mauro Brunato, started writing with an introduction called Learning and Intelligent Optimization or LION, which is the ultimate combination of learning that provides you lessons from optimization and data applied to solving complex issues. The book is all about enhancing the automation level and linking the data to discussions and actions directly.

3) Introduction to Machine Learning

Author Amnon Shashua has exceptionally written this free eBook on Machine Learning that comes with an introduction that covers the Statistical Interference like EM, ML, bayes, MXEnt duality and spectral methods and algebraic methods. The introduction also covers the PAC learning models including Dual Sampling theorem, VC Dimension and Formal Model.

4) Bayesian Reasoning and Machine Learning

This free eBook was written by David Barber in the year 2011 and the book is primarily designed for final year undergraduate students having restricted background in linear algebra and calculus. Lucid and comprehensive, this free eBook covers almost everything from fundamentals to more advanced methods required for the framework of graphical models.

5) The Elements of Statistical Learning: Data Mining, Inference, and Prediction

T. Hastie, R. Tibshirani, J. Friedman wrote this free eBook on Machine Learning and the introduction of the book bring to you some of the crucial and fresh ideas in machine learning and even explains the best statistical framework. The authors of this eBook specifically focus on different methods of designing robotics and their conceptual foundations, instead of focusing on theoretical properties.

6) Reinforcement Learning

The authors of this free eBook cover all the crucial aspects of machine learning right from extending to describing the scope of underpinning learning. The book also explains that there is always a wider usage of different fields and the concept of reinforcement learning will help the students to manage control tasks and its complexity.

These were some of the best eBook on Machine Learning that you may use to learn more about robotics and artificial intelligence. All these books are available online in downloadable version. So, start with these books and kick start your career in robotics and artificial intelligence.

7) Information Theory,Inference, and Learing

This book is written by David J. C. MacKay.Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering – communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.

8) Inductive Logic Programming: Techniques and Applications

The book covers empirical inductive logic programming, one of the two major subfields of ILP, which has shown its application potential in the following areas: knowledge acquisition, inductive program synthesis, inductive data engineering, and knowledge discovery in databases. The book is intended for knowledge engineers concerned with the automatic synthesis of knowledge bases for expert systems, software engineers who could profit from inductive programming tools, researchers in system development and database methodology, interested in techniques for knowledge discovery in databases and inductive data engineering, and researchers and graduates in artificial intelligence, machine learning, logic programming, software engineering and database methodology.

9) Machine Learning, Neural and Statistical Classification

The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems. As the book’s title suggests. a wide variety of approaches has been taken towards this task. Three main historical strands of research can be identified: statistical, machine learning and neural network.

10) Gaussian Processes for Machine Learning

Gaussian processes (GPs) provide a  practical, probabilistic approach to learning in kernel machines. The book interacts with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others.

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Thanks for stashing this -- so much to learn!

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