Welcome to E-Books Directory
This page lists freely downloadable books.

 

e-books in this category

A Survey of Statistical Network ModelsA Survey of Statistical Network Models
by A. Goldenberg, A.X. Zheng, S.E. Fienberg, E.M. Airoldi - arXiv , 2009
We begin with the historical development of statistical network modeling and then we introduce some examples in the network literature. Our subsequent discussion focuses on prominent static and dynamic network models and their interconnections.
(247 views)

Machine Learning: The Complete GuideMachine Learning: The Complete Guide
- Wikipedia , 2014
Contents: Introduction and Main Principles; Background and Preliminaries; Knowledge discovery in Databases; Reasoning; Search Methods; Statistics; Main Learning Paradigms; Classification Tasks; Online Learning; Semi-supervised learning; etc.
(495 views)

Introduction to Machine LearningIntroduction to Machine Learning
by Alex Smola, S.V.N. Vishwanathan - Cambridge University Press , 2008
Over the past two decades Machine Learning has become one of the mainstays of information technology and a rather central part of our life. Smart data analysis will become even more pervasive as a necessary ingredient for technological progress.
(1223 views)

An Introductory Study on Time Series Modeling and ForecastingAn Introductory Study on Time Series Modeling and Forecasting
by Ratnadip Adhikari, R. K. Agrawal - arXiv , 2013
This work presents a concise description of some popular time series forecasting models used in practice, with their features. We describe three important classes of time series models, viz. the stochastic, neural networks and SVM based models.
(1044 views)

The LION Way: Machine Learning plus Intelligent OptimizationThe LION Way: Machine Learning plus Intelligent Optimization
by Roberto Battiti, Mauro Brunato - Lionsolver, Inc. , 2013
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.
(5449 views)

A Course in Machine LearningA Course in Machine Learning
by Hal Daumé III - ciml.info , 2012
Tis 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.
(4336 views)

A First Encounter with Machine LearningA First Encounter with Machine Learning
by Max Welling - University of California Irvine , 2011
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.
(3793 views)

Bayesian Reasoning and Machine LearningBayesian Reasoning and Machine Learning
by David Barber - Cambridge University Press , 2011
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.
(7743 views)

Introduction to Machine LearningIntroduction to Machine Learning
by Amnon Shashua - arXiv , 2009
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).
(5827 views)

The Elements of Statistical Learning: Data Mining, Inference, and PredictionThe Elements of Statistical Learning: Data Mining, Inference, and Prediction
by T. Hastie, R. Tibshirani, J. Friedman - Springer , 2009
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.
(9304 views)

Reinforcement LearningReinforcement Learning
by C. Weber, M. Elshaw, N. M. Mayer - InTech , 2008
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.
(5571 views)

Machine LearningMachine Learning
by Abdelhamid Mellouk, Abdennacer Chebira - InTech , 2009
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.
(7373 views)

How Are We To Know?How Are We To Know?
by Nils J. Nilsson - Stanford University , 2006
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.
(5496 views)

Reinforcement Learning: An IntroductionReinforcement Learning: An Introduction
by Richard S. Sutton, Andrew G. Barto - The MIT Press , 1998
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.
(6605 views)

Gaussian Processes for Machine LearningGaussian Processes for Machine Learning
by Carl E. Rasmussen, Christopher K. I. Williams - The MIT Press , 2005
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.
(6249 views)

Machine Learning, Neural and Statistical ClassificationMachine Learning, Neural and Statistical Classification
by D. Michie, D. J. Spiegelhalter - Ellis Horwood , 1994
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.
(9448 views)

Introduction To Machine LearningIntroduction To Machine Learning
by Nils J Nilsson , 1997
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.
(6774 views)

Inductive Logic Programming: Techniques and ApplicationsInductive Logic Programming: Techniques and Applications
by Nada Lavrac, Saso Dzeroski - Prentice Hall , 1994
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.
(9954 views)

Practical Artificial Intelligence Programming in JavaPractical Artificial Intelligence Programming in Java
by Mark Watson - Lulu.com , 2008
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).
(15932 views)

Information Theory, Inference, and Learning AlgorithmsInformation Theory, Inference, and Learning Algorithms
by David J. C. MacKay - Cambridge University Press , 2003
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.
(10520 views)