Machine Learning Foundations
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Deep Learning
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Author : Christopher M. Bishop
language : en
Publisher: Springer Nature
Release Date : 2023-11-01
Deep Learning written by Christopher M. Bishop and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-01 with Computers categories.
This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code. Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University. “Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.” -- Geoffrey Hinton "With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." – Yann LeCun “This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.” -- Yoshua Bengio
Foundations Of Machine Learning Second Edition
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Author : Mehryar Mohri
language : en
Publisher: MIT Press
Release Date : 2018-12-25
Foundations Of Machine Learning Second Edition written by Mehryar Mohri and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-25 with Computers categories.
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.
Artificial Intelligence And Machine Learning Foundations
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Author : Andrew Lowe
language : en
Publisher: BCS, the Chartered Institute for IT
Release Date : 2024-10-28
Artificial Intelligence And Machine Learning Foundations written by Andrew Lowe and has been published by BCS, the Chartered Institute for IT this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-28 with Computers categories.
In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today to support products, services, science and engineering, and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed. You will delve into the theory behind AI and machine learning projects, examining techniques for learning from data, the use of neural networks and why algorithms are so important in the development of a new AI agent or system.
Imbalanced Learning
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Author : Haibo He
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-07
Imbalanced Learning written by Haibo He 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 2013-06-07 with Technology & Engineering categories.
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.
Handbook Of Research On Machine Learning
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Author : Monika Mangla
language : en
Publisher: CRC Press
Release Date : 2022-08-04
Handbook Of Research On Machine Learning written by Monika Mangla and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-04 with Computers categories.
This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation. The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.
Machine Learning Foundations
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Author : Taeho Jo
language : en
Publisher: Springer Nature
Release Date : 2021-02-12
Machine Learning Foundations written by Taeho Jo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-12 with Technology & Engineering categories.
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.
Machine Learning Foundations And Applications
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Author : Jarrel E
language : en
Publisher:
Release Date : 2025-05-10
Machine Learning Foundations And Applications written by Jarrel E and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-10 with Computers categories.
Master the algorithms powering today's AI revolution. This practical guide breaks down the foundations of machine learning into clear, structured lessons-covering supervised learning, unsupervised learning, and reinforcement learning. Whether you're a student, developer, or data professional, you'll learn how real-world models like linear regression, neural networks, support vector machines, PCA, and Q-learning actually work-mathematically and computationally. This book blends theory with implementation, offering step-by-step explanations, intuitive insights, and practical tools for applying machine learning in business, research, and product development. If you're serious about learning machine learning, this is the book that takes you from first principles to advanced concepts-with clarity, depth, and purpose.
Deep Learning Foundations
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Author : Taeho Jo
language : en
Publisher: Springer Nature
Release Date : 2023-07-25
Deep Learning Foundations written by Taeho Jo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-25 with Technology & Engineering categories.
This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.
Artificial Intelligence Foundations
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Author : Andrew Lowe
language : en
Publisher: BCS, The Chartered Institute for IT
Release Date : 2020-08-24
Artificial Intelligence Foundations written by Andrew Lowe and has been published by BCS, The Chartered Institute for IT this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-24 with categories.
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.
Machine Learning Foundations And Applications
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Author : Jarrel E
language : en
Publisher:
Release Date : 2025-05-09
Machine Learning Foundations And Applications written by Jarrel E and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-09 with Computers categories.
Master the algorithms powering today's AI revolution. This practical guide breaks down the foundations of machine learning into clear, structured lessons-covering supervised learning, unsupervised learning, and reinforcement learning. Whether you're a student, developer, or data professional, you'll learn how real-world models like linear regression, neural networks, support vector machines, PCA, and Q-learning actually work-mathematically and computationally. This book blends theory with implementation, offering step-by-step explanations, intuitive insights, and practical tools for applying machine learning in business, research, and product development. If you're serious about learning machine learning, this is the book that takes you from first principles to advanced concepts-with clarity, depth, and purpose.