Deep Learning (4)
e-books in Machine Learning category
by Patrick Hebron - O'Reilly Media , 2016
This book introduces you to contemporary machine learning systems and helps you integrate machine-learning capabilities into your user-facing designs. Patrick Hebron explains how machine-learning applications can affect the way you design websites.
by David Beyer - O'Reilly Media , 2016
The series of interviews in this exclusive report unpack concepts and innovations that represent the frontiers of ever-smarter machines. You’ll get a rare glimpse into this exciting field through the eyes of some of its leading minds.
by Robert E. Schapire, Yoav Freund - The MIT Press , 2014
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
by Jonas Buchli, et al. - arXiv.org , 2017
The starting point is the formulation of of an optimal control problem and deriving the different types of solutions and algorithms from there. These lecture notes aim at supporting this unified view with a unified notation wherever possible.
by Owain Evans, et al. - AgentModels.org , 2017
This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning. One motivation is to create richer models of human planning, which capture human biases. The book assumes basic programming experience.
by Gianluca Bontempi, Souhaib Ben Taieb - OTexts , 2013
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. This manuscript aims to find a good balance between theory and practice.
by Alexander Rakhlin, Karthik Sridharan - University of Pennsylvania , 2014
This text focuses on theoretical aspects of Statistical Learning and Sequential Prediction. The minimax approach, which we emphasize throughout the course, offers a systematic way of comparing learning problems. We will discuss learning algorithms...
by Shai Shalev-Shwartz, Shai Ben-David - Cambridge University Press , 2014
This book introduces machine learning and the algorithmic paradigms it offers. It provides a theoretical account of the fundamentals underlying machine learning and mathematical derivations that transform these principles into practical algorithms.
by Zdravko Markov - Central Connecticut State University , 2003
Contents: Introduction; Concept learning; Languages for learning; Version space learning; Induction of Decision trees; Covering strategies; Searching the generalization / specialization graph; Inductive Logic Progrogramming; Unsupervised Learning ...
by Aaron Hertzmann - University of Toronto , 2010
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.
by Yoshua Bengio - Now Publishers , 2009
This book discusses the principles of learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models.
by G. James, D. Witten, T. Hastie, R. Tibshirani - Springer , 2013
This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.
by Csaba Szepesvari - Morgan and Claypool Publishers , 2009
We focus on those algorithms of reinforcement learning that build on the theory of dynamic programming. We give a comprehensive catalog of learning problems, describe the core ideas, followed by the discussion of their properties and limitations.
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.
- 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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
by Richard S. Sutton, Andrew G. Barto - The MIT Press , 2017
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.
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.
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.
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.
by Stephen Muggleton, Luc de Raedt - ScienceDirect , 1994
Inductive Logic Programming is a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge. The authors survey the most important theories and methods of this new field.
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).
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.