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Machine Learning

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Deep Learning (6)

e-books in Machine Learning category

Book cover: Machine Learning: A Probabilistic PerspectiveMachine Learning: A Probabilistic Perspective
by - The MIT Press ,
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
(4206 views)
Book cover: Introduction to Machine Learning for the SciencesIntroduction to Machine Learning for the Sciences
by - arXiv.org ,
This is an introductory machine learning course specifically developed with STEM students in mind, written by the theoretical Condensed Matter Theory group at the University of Zurich. We discuss supervised, unsupervised, and reinforcement learning.
(3552 views)

Book cover: Foundations of Machine LearningFoundations of Machine Learning
by - The MIT Press ,
This is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools.
(6762 views)
Book cover: The Hundred-Page Machine Learning BookThe Hundred-Page Machine Learning Book
by ,
This is the first successful attempt to write an easy to read book on machine learning that isn't afraid of using math. It's also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.
(9736 views)
Book cover: Reinforcement Learning and Optimal ControlReinforcement Learning and Optimal Control
by - Athena Scientific ,
The book considers large and challenging multistage decision problems, which can be solved by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations.
(10139 views)
Book cover: An Introduction to Probabilistic ProgrammingAn Introduction to Probabilistic Programming
by - arXiv.org ,
This text is designed to be a graduate-level introduction to probabilistic programming. It provides a thorough background for anyone wishing to use a probabilistic programming system, and introduces the techniques needed to build these systems.
(5282 views)
Book cover: A Brief Introduction to Machine Learning for EngineersA Brief Introduction to Machine Learning for Engineers
by - arXiv.org ,
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(7260 views)
Book cover: Machine Learning for Data StreamsMachine Learning for Data Streams
by - The MIT Press ,
This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA, allowing readers to try out the techniques after reading the explanations.
(7037 views)
Book cover: Elements of Causal Inference: Foundations and Learning AlgorithmsElements of Causal Inference: Foundations and Learning Algorithms
by - The MIT Press ,
This book offers a self-contained and concise introduction to causal models and how to learn them from data. The book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from data ...
(6629 views)
Book cover: Machine Learning for DesignersMachine Learning for Designers
by - O'Reilly Media ,
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.
(7400 views)
Book cover: Boosting: Foundations and AlgorithmsBoosting: Foundations and Algorithms
by - The MIT Press ,
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.
(6903 views)
Book cover: Optimal and Learning Control for Autonomous RobotsOptimal and Learning Control for Autonomous Robots
by - arXiv.org ,
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.
(6086 views)
Book cover: Modeling Agents with Probabilistic ProgramsModeling Agents with Probabilistic Programs
by - AgentModels.org ,
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.
(6159 views)
Book cover: Statistical Foundations of Machine LearningStatistical Foundations of Machine Learning
by ,
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.
(9603 views)
Book cover: Statistical Learning and Sequential PredictionStatistical Learning and Sequential Prediction
by - University of Pennsylvania ,
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...
(7013 views)
Book cover: Understanding Machine Learning: From Theory to AlgorithmsUnderstanding Machine Learning: From Theory to Algorithms
by - Cambridge University Press ,
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.
(10894 views)
Book cover: Lecture Notes in Machine LearningLecture Notes in Machine Learning
by - Central Connecticut State University ,
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 ...
(9610 views)
Book cover: Machine Learning and Data Mining: Lecture NotesMachine Learning and Data Mining: Lecture Notes
by - University of Toronto ,
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.
(10430 views)
Book cover: Learning Deep Architectures for AILearning Deep Architectures for AI
by - Now Publishers ,
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.
(8422 views)
Book cover: An Introduction to Statistical LearningAn Introduction to Statistical Learning
by - Springer ,
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.
(10403 views)
Book cover: Algorithms for Reinforcement LearningAlgorithms for Reinforcement Learning
by - Morgan and Claypool Publishers ,
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.
(8271 views)
Book cover: A Survey of Statistical Network ModelsA Survey of Statistical Network Models
by - arXiv ,
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.
(8976 views)
Book cover: Introduction to Machine LearningIntroduction to Machine Learning
by - Cambridge University Press ,
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.
(10219 views)
Book cover: An Introductory Study on Time Series Modeling and ForecastingAn Introductory Study on Time Series Modeling and Forecasting
by - arXiv ,
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.
(12444 views)
Book cover: The LION Way: Machine Learning plus Intelligent OptimizationThe LION Way: Machine Learning plus Intelligent Optimization
by - Lionsolver, Inc. ,
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.
(36985 views)
Book cover: A Course in Machine LearningA Course in Machine Learning
by - ciml.info ,
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.
(22607 views)
Book cover: A First Encounter with Machine LearningA First Encounter with Machine Learning
by - University of California Irvine ,
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.
(13083 views)
Book cover: Bayesian Reasoning and Machine LearningBayesian Reasoning and Machine Learning
by - Cambridge University Press ,
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.
(23291 views)
Book cover: Introduction to Machine LearningIntroduction to Machine Learning
by - arXiv ,
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).
(22916 views)
Book cover: Reinforcement LearningReinforcement Learning
by - InTech ,
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.
(21899 views)
Book cover: Machine LearningMachine Learning
by - InTech ,
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.
(16835 views)
Book cover: Reinforcement Learning: An IntroductionReinforcement Learning: An Introduction
by - The MIT Press ,
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.
(27839 views)
Book cover: Gaussian Processes for Machine LearningGaussian Processes for Machine Learning
by - The MIT Press ,
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.
(28475 views)
Book cover: Machine Learning, Neural and Statistical ClassificationMachine Learning, Neural and Statistical Classification
by - Ellis Horwood ,
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.
(28822 views)
Book cover: Introduction To Machine LearningIntroduction To Machine Learning
by ,
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.
(30462 views)
Book cover: Inductive Logic Programming: Theory and MethodsInductive Logic Programming: Theory and Methods
by - ScienceDirect ,
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.
(36443 views)
Book cover: Practical Artificial Intelligence Programming in JavaPractical Artificial Intelligence Programming in Java
by - Lulu.com ,
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).
(25440 views)
Book cover: Information Theory, Inference, and Learning AlgorithmsInformation Theory, Inference, and Learning Algorithms
by - Cambridge University Press ,
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.
(29802 views)