Machine Learning

e-books in Machine Learning category

Machine Learning: A Probabilistic Perspective
by Kevin Patrick Murphy - The MIT Press , 2012
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.
(3687 views)

Introduction to Machine Learning for the Sciences
by Titus Neupert, et al. - arXiv.org , 2021
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.
(2955 views)

Foundations of Machine Learning
by M. Mohri, A. Rostamizadeh, A. Talwalkar - The MIT Press , 2018
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.
(6191 views)

The Hundred-Page Machine Learning Book
by Andriy Burkov , 2019
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.
(8717 views)

Reinforcement Learning and Optimal Control
by Dimitri P. Bertsekas - Athena Scientific , 2019
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.
(9405 views)

An Introduction to Probabilistic Programming
by Jan-Willem van de Meent, et al. - arXiv.org , 2018
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.
(4806 views)

A Brief Introduction to Machine Learning for Engineers
by Osvaldo Simeone - arXiv.org , 2017
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.
(6665 views)

Machine Learning for Data Streams
by Albert Bifet, et al. - The MIT Press , 2017
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.
(6418 views)

Elements of Causal Inference: Foundations and Learning Algorithms
by J. Peters, D. Janzing, B. Schölkopf - The MIT Press , 2017
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 ...
(6027 views)

Machine Learning for Designers
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.
(6719 views)

Boosting: Foundations and Algorithms
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.
(6478 views)

Optimal and Learning Control for Autonomous Robots
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.
(5673 views)

Modeling Agents with Probabilistic Programs
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.
(5699 views)

Statistical Foundations of Machine Learning
by Gianluca Bontempi, Souhaib Ben Taieb , 2017
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.
(9109 views)

Statistical Learning and Sequential Prediction
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...
(6596 views)

Understanding Machine Learning: From Theory to 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.
(10133 views)

Lecture Notes in Machine Learning
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 ...
(9159 views)

Machine Learning and Data Mining: Lecture Notes
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.
(9980 views)

Learning Deep Architectures for AI
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.
(7791 views)

An Introduction to Statistical Learning
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.
(9836 views)

Algorithms for Reinforcement Learning
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.
(7745 views)

A 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.
(8370 views)

Introduction 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.
(9636 views)

An 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.
(11745 views)

The 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.
(35506 views)

A 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.
(21484 views)

A 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.
(12131 views)

Bayesian 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.
(22415 views)

Introduction 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).
(22074 views)

The 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.
(40061 views)

Reinforcement 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.
(21219 views)

Machine 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.
(16206 views)

Reinforcement Learning: An Introduction
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.
(26864 views)

Gaussian 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.
(27664 views)

Machine 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.
(27882 views)

Introduction 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.
(29385 views)

Inductive Logic Programming: Theory and Methods
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.
(35257 views)

Practical 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).
(24542 views)

Information 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.
(28737 views)