A Concise Introduction To Machine Learning
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Machine Learning
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Author : Steven W. Knox
language : en
Publisher: John Wiley & Sons
Release Date : 2018-04-17
Machine Learning written by Steven W. Knox and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-17 with Computers categories.
AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS PROSE Award Finalist 2019 Association of American Publishers Award for Professional and Scholarly Excellence Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. This important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods Presents R source code which shows how to apply and interpret many of the techniques covered Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions Contains useful information for effectively communicating with clients A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning. STEVEN W. KNOX holds a Ph.D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years’ experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency.
A Concise Introduction To Machine Learning
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Author : A. C. Faul
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2019-08-07
A Concise Introduction To Machine Learning written by A. C. Faul and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-07 with Business & Economics categories.
A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of machine learning concepts from basic principles, and illustrates every concept using examples in MATLAB.
A Concise Introduction To Machine Learning
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Author : A. C Faul
language : en
Publisher:
Release Date : 2025
A Concise Introduction To Machine Learning written by A. C Faul and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with Computers categories.
A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of machine learning concepts from basic principles and illustrates every concept using examples in both Python and MATLAB(R), which are available on GitHub and can be run from there in Binder in a web browser. Each chapter concludes with exercises to explore the content. The emphasis of the book is on the question of Why--only if "why" an algorithm is successful is understood, can it be properly applied and the results trusted. Standard techniques are treated rigorously, including an introduction to the necessary probability theory. This book addresses the commonalities of methods, aims to give a thorough and in-depth treatment and develop intuition for the inner workings of algorithms, while remaining concise. This useful reference should be essential on the bookshelf of anyone employing machine learning techniques, since it is born out of strong experience in university teaching and research on algorithms, while remaining approachable and readable.
A Concise Introduction To Multiagent Systems And Distributed Artificial Intelligence
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Author : Nikos Vlassis
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2007
A Concise Introduction To Multiagent Systems And Distributed Artificial Intelligence written by Nikos Vlassis and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
A Concise Introduction To Machine Learning
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Author : Anita C. Faul
language : en
Publisher:
Release Date : 2025
A Concise Introduction To Machine Learning written by Anita C. Faul and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with Machine learning categories.
"A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of machine learning concepts from basic principles and illustrates every concept using examples in both Python and Matlaba which are available on GitHub and can be run from there in Binder in a web browser. Each chapter concludes with exercises to explore the content. The emphasis of the book is on the question of Why - only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Standard techniques are treated rigorously, including an introduction to the necessary probability theory. This book addresses the commonalities and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise. This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques, since it is born out of strong experience in university teaching and research on algorithms, while remaining approachable and readable"--
An Introduction To Machine Learning
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Author : Gopinath Rebala
language : en
Publisher:
Release Date : 2019
An Introduction To Machine Learning written by Gopinath Rebala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Machine learning categories.
Just like electricity, Machine Learning will revolutionize our life in many ways - some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with.
Machine Learning
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Author : Steven W. Knox
language : en
Publisher: John Wiley & Sons
Release Date : 2018-03-15
Machine Learning written by Steven W. Knox and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-15 with Computers categories.
AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS PROSE Award Finalist 2019 Association of American Publishers Award for Professional and Scholarly Excellence Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. This important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods Presents R source code which shows how to apply and interpret many of the techniques covered Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions Contains useful information for effectively communicating with clients A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning. STEVEN W. KNOX holds a Ph.D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years’ experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency.
Introduction To Machine Learning
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Author : Ethem Alpaydin
language : en
Publisher: MIT Press
Release Date : 2014-08-22
Introduction To Machine Learning written by Ethem Alpaydin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-22 with Computers categories.
Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
Machine Learning Fundamentals
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Author : Hui Jiang
language : en
Publisher: Cambridge University Press
Release Date : 2021-11-25
Machine Learning Fundamentals written by Hui Jiang and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-25 with Computers categories.
A coherent introduction to core concepts and deep learning techniques that are critical to academic research and real-world applications.
Introduction To Artificial Intelligence
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Author : Wolfgang Ertel
language : en
Publisher: Springer
Release Date : 2018-01-18
Introduction To Artificial Intelligence written by Wolfgang Ertel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-18 with Computers categories.
This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning. Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW). Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.