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Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 0262351366
Category : Computers
Languages : en
Pages : 505
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Book Description
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book 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 needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 0262351366
Category : Computers
Languages : en
Pages : 505
Get Book
Book Description
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book 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 needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Author: Taeho Jo
Publisher: Springer Nature
ISBN: 3030659003
Category : Technology & Engineering
Languages : en
Pages : 391
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Book Description
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
Author: Andrew Lowe
Publisher: BCS, The Chartered Institute for IT
ISBN: 9781780175287
Category :
Languages : en
Pages : 160
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Book Description
In line with the BCS AI Foundation and Essentials certificates, this book guides you through the world of AI. You will learn how AI is being utilised today, and how it is likely to be used in the future. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Jon Adams
Publisher: Green Mountain Computing
ISBN:
Category : Computers
Languages : en
Pages : 117
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Book Description
AI Foundations of Machine Learning Embark on a clarifying expedition through the vibrant world of AI with "AI Foundations of Machine Learning." This comprehensive guide is meticulously crafted for those eager to unravel the complex mechanisms driving artificial intelligence and for pioneers looking to grasp the foundational stones of future technological advancements. From the fundamentals to the futuristic prospects, this book serves as both an educational journey and an initiation into the realm where data, computation, and potential converge. Contents: Understanding Supervised Learning: Begin your journey with an exploration of supervised learning, where machines learn from data with known outcomes, setting the stage for further complexities. The Mechanics of Unsupervised Learning: Delve into the artistry of AI as it uncovers hidden patterns without explicit instructions, highlighting the autonomy of machine learning. Diving into Neural Networks: Uncover the intricacies of neural networks, AI's approximation of the human brain, capable of recognizing speech, images, and nuances in vast datasets. The Decision Tree Paradigm: Discover the decision-making processes of AI through the decision tree paradigm, where data is systematically divided and conquered. Ensemble Methods Combining Strengths: Learn about the power of ensemble methods, which combine multiple models to enhance predictive accuracy and overcome individual weaknesses. Evaluating Model Performance: Understand the critical aspect of evaluating AI model performance, ensuring the integrity and applicability of machine learning applications. Machine Learning in the Real World: Witness the transformative impact of machine learning across various industries, from healthcare to finance, and how it reshapes our interaction with technology. The Future of Machine Learning: Gaze into the future, anticipating the breakthroughs and challenges of machine learning as it becomes an omnipresent force in our lives. This book is your gateway to understanding and participating in the future of AI, equipped with the knowledge to navigate and contribute to the advancements that lie ahead. Whether you are a student, professional, or enthusiast, "AI Foundations of Machine Learning" offers valuable insights into the ever-evolving field of machine learning, encouraging readers to not only understand but also to innovate in the unfolding story of AI.
Author: David L. Poole
Publisher: Cambridge University Press
ISBN: 110719539X
Category : Computers
Languages : en
Pages : 821
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Book Description
Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.
Author: Thomas D. Grant
Publisher: Springer Nature
ISBN: 3030435822
Category : Social Science
Languages : en
Pages : 163
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Book Description
This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data.
Author: Jon Krohn
Publisher: Addison-Wesley Professional
ISBN: 0135121728
Category : Computers
Languages : en
Pages : 725
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Book Description
"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Author: Valentin K. Ivanov
Publisher: Walter de Gruyter
ISBN: 3110944820
Category : Mathematics
Languages : en
Pages : 296
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Book Description
This monograph is a revised and extended version of the Russian edition from 1978. It includes the general theory of linear ill-posed problems concerning e. g. the structure of sets of uniform regularization, the theory of error estimation, and the optimality method. As a distinguishing feature the book considers ill-posed problems not only in Hilbert but also in Banach spaces. It is natural that since the appearance of the first edition considerable progress has been made in the theory of inverse and ill-posed problems as wall as in ist applications. To reflect these accomplishments the authors included additional material e. g. comments to each chapter and a list of monographs with annotations.
Author: Michael R. Genesereth
Publisher: Morgan Kaufmann
ISBN: 0128015543
Category : Computers
Languages : en
Pages : 427
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Book Description
Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intelligence is a lucid, rigorous, and comprehensive account of the fundamentals of artificial intelligence from the standpoint of logic. The first section of the book introduces the logicist approach to AI--discussing the representation of declarative knowledge and featuring an introduction to the process of conceptualization, the syntax and semantics of predicate calculus, and the basics of other declarative representations such as frames and semantic nets. This section also provides a simple but powerful inference procedure, resolution, and shows how it can be used in a reasoning system. The next several chapters discuss nonmonotonic reasoning, induction, and reasoning under uncertainty, broadening the logical approach to deal with the inadequacies of strict logical deduction. The third section introduces modal operators that facilitate representing and reasoning about knowledge. This section also develops the process of writing predicate calculus sentences to the metalevel--to permit sentences about sentences and about reasoning processes. The final three chapters discuss the representation of knowledge about states and actions, planning, and intelligent system architecture. End-of-chapter bibliographic and historical comments provide background and point to other works of interest and research. Each chapter also contains numerous student exercises (with solutions provided in an appendix) to reinforce concepts and challenge the learner. A bibliography and index complete this comprehensive work.
