Knowledge Guided Machine Learning

Author : Anuj Karpatne,Ramakrishnan Kannan,Vipin Kumar
Publisher : CRC Press
Page : 442 pages
File Size : 43,5 Mb
Release : 2022-08-15
Category : Business & Economics
ISBN : 9781000598100

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Knowledge Guided Machine Learning by Anuj Karpatne,Ramakrishnan Kannan,Vipin Kumar Pdf

Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

Author : Anuj Karpatne,Ramakrishnan Kannan,Vipin Kumar
Publisher : CRC Press
Page : 520 pages
File Size : 48,7 Mb
Release : 2022-08-15
Category : Business & Economics
ISBN : 9781000598131

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Knowledge Guided Machine Learning by Anuj Karpatne,Ramakrishnan Kannan,Vipin Kumar Pdf

Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

Author : Tom M. Mitchell,Jaime G. Carbonell,Ryszard S. Michalski
Publisher : Springer Science & Business Media
Page : 413 pages
File Size : 45,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461322795

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Machine Learning by Tom M. Mitchell,Jaime G. Carbonell,Ryszard S. Michalski Pdf

One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.

Author : Tom Michael Mitchell
Publisher : Unknown
Page : 429 pages
File Size : 48,8 Mb
Release : 1986
Category : Electronic
ISBN : OCLC:636673622

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Machine learning by Tom Michael Mitchell Pdf

Author : Samuel Hack
Publisher : Samuel Hack
Page : 220 pages
File Size : 54,6 Mb
Release : 2021-03-07
Category : Electronic
ISBN : 1801728569

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Machine Learning for Beginners by Samuel Hack Pdf

TODAY ONLY 55% OFF for Bookstores! Are you interested in learning about the amazing capabilities of machine learning, but you're worried it will be just too complicated? Or are you a programmer looking for a solid introduction into this field? Your customers must have this guide to understand the hidden secrets of artificial intelligence! Machine learning is an incredible technology which we're only just beginning to understand. Those who break into this industry early will reap the rewards as this field grows more and more important to businesses the world over. And the good news is, it's not too late to start! This guide breaks down the fundamentals of machine learning in a way that anyone can understand. With reference to the different kinds of machine learning models, neural networks, and the way these models learn data, you'll find everything you need to know to get started with machine learning in a concise, easy-to-understand way. Here's what you'll discover inside: What is Artificial Intelligence Really, and Why is it So Powerful? Choosing the Right Kind of Machine Learning Model for You An Introduction to Statistics Supervised and Unsupervised Learning The Power of Neural Networks Reinforcement Learning and Ensemble Modeling "Random Forests" and Decision Trees Must-Have Programming Tools And Much More! Whether you're already a programmer or if you're a complete beginner, now you can break into machine learning in no time! Covering all the basics from simple decision trees to the complex decision-making processes which mirror our own brains, Machine Learning for Beginners is your comprehensive introduction to this amazing field! Buy it NOW and let your customers become to addicted to this incredible book!

Author : Ryan Knight
Publisher : Ryan Knight
Page : 48 pages
File Size : 45,9 Mb
Release : 2024-05-08
Category : Computers
ISBN : 8210379456XXX

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Machine Learning for Beginners by Ryan Knight Pdf

Enter a world of algorithms, data, and artificial intelligence, this all-inclusive guide strips away the complexity of machine learning and AI, transforming them from daunting subjects into accessible and comprehendible concepts. Whether you're a total novice or a professional looking to broaden your knowledge, this guide provides a structured approach that walks you through the basics, right through to the cutting-edge applications of AI and machine learning. Crafted with the reader in mind, every chapter provides detailed explanations, relatable examples, and step-by-step instructions to ensure a comprehensive yet enjoyable learning experience. Inside this book, you'll discover: An introduction to the exciting world of machine learning and AI, making it accessible to everyone regardless of technical background. Comprehensive discussions on the foundational concepts of machine learning, including algorithms, data science principles, and the different types of machine learning. Deep dives into the transformative applications of AI and machine learning in industries such as healthcare, retail, finance, transportation, education, and entertainment. Practical guides on mastering the essential tools and techniques for building intelligent solutions, complete with hands-on exercises and examples. An exploration of the ethical considerations around AI and machine learning, and the responsibilities we have as practitioners. Future trends in machine learning and AI, providing a glimpse into what lies on the horizon. Ignite your journey into the fascinating world of machine learning and AI today. Unleash the power of data and algorithms, create intelligent solutions, and shape a better future. Are you ready to master the future? The opportunity is just a click away. Pick up your copy now, and let's get started!

Author : Pierre Marquis,Odile Papini,Henri Prade
Publisher : Springer Nature
Page : 808 pages
File Size : 41,7 Mb
Release : 2020-05-08
Category : Technology & Engineering
ISBN : 9783030061647

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A Guided Tour of Artificial Intelligence Research by Pierre Marquis,Odile Papini,Henri Prade Pdf

The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Then the volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, reasoning based on similarity (case-based, interpolative, or analogical), as well as reasoning about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statistical computational learning and reinforcement learning (other machine learning topics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.

Author : Frank Hutter,Lars Kotthoff,Joaquin Vanschoren
Publisher : Springer
Page : 223 pages
File Size : 41,6 Mb
Release : 2019-05-17
Category : Computers
ISBN : 9783030053185

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Automated Machine Learning by Frank Hutter,Lars Kotthoff,Joaquin Vanschoren Pdf

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Author : Hemachandran K,Raul V. Rodriguez,Umashankar Subramaniam,Valentina Emilia Balas
Publisher : CRC Press
Page : 387 pages
File Size : 51,6 Mb
Release : 2023-09-29
Category : Computers
ISBN : 9781000934595

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Artificial Intelligence and Knowledge Processing by Hemachandran K,Raul V. Rodriguez,Umashankar Subramaniam,Valentina Emilia Balas Pdf

Artificial Intelligence and Knowledge Processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories. This book acts as a guide and blends the basics of Artificial Intelligence in various domains, which include Machine Learning, Deep Learning, Artificial Neural Networks, and Expert Systems, and extends their application in all sectors. Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, discusses the designing of new AI algorithms used to convert general applications to AI-based applications. It highlights different Machine Learning and Deep Learning models for various applications used in healthcare and wellness, agriculture, and automobiles. The book offers an overview of the rapidly growing and developing field of AI applications, along with Knowledge of Engineering, and Business Analytics. Real-time case studies are included across several different fields such as Image Processing, Text Mining, Healthcare, Finance, Digital Marketing, and HR Analytics. The book also introduces a statistical background and probabilistic framework to enhance the understanding of continuous distributions. Topics such as Ensemble Models, Deep Learning Models, Artificial Neural Networks, Expert Systems, and Decision-Based Systems round out the offerings of this book. This multi-contributed book is a valuable source for researchers, academics, technologists, industrialists, practitioners, and all those who wish to explore the applications of AI, Knowledge Processing, Deep Learning, and Machine Learning.

Author : Pierre Marquis,Odile Papini,Henri Prade
Publisher : Springer Nature
Page : 584 pages
File Size : 45,9 Mb
Release : 2020-05-08
Category : Technology & Engineering
ISBN : 9783030061708

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A Guided Tour of Artificial Intelligence Research by Pierre Marquis,Odile Papini,Henri Prade Pdf

The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This third volume is dedicated to the interfaces of AI with various fields, with which strong links exist either at the methodological or at the applicative levels. The foreword of this volume reminds us that AI was born for a large part from cybernetics. Chapters are devoted to disciplines that are historically sisters of AI: natural language processing, pattern recognition and computer vision, and robotics. Also close and complementary to AI due to their direct links with information are databases, the semantic web, information retrieval and human-computer interaction. All these disciplines are privileged places for applications of AI methods. This is also the case for bioinformatics, biological modeling and computational neurosciences. The developments of AI have also led to a dialogue with theoretical computer science in particular regarding computability and complexity. Besides, AI research and findings have renewed philosophical and epistemological questions, while their cognitive validity raises questions to psychology. The volume also discusses some of the interactions between science and artistic creation in literature and in music. Lastly, an epilogue concludes the three volumes of this Guided Tour of AI Research by providing an overview of what has been achieved by AI, emphasizing AI as a science, and not just as an innovative technology, and trying to dispel some misunderstandings.

Author : Steven Alex
Publisher : Unknown
Page : 135 pages
File Size : 44,8 Mb
Release : 2019-11-06
Category : Electronic
ISBN : 1706195850

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Machine Learning by Steven Alex Pdf

★ ★ Buy the Paperback Version of this Book and Get the Kindle Book version for FREE ★ ★ Machine Learning (Update Edition 2019-2020) this Guide is a branch of artificial intelligence, This Machine Learning Series idea is relatively new. A science that researches machines to acquire new knowledge and new skills and to identify existing knowledge. The best way to understand the potential of machine learning is to explore how people and companies are currently taking advantage of it.If you are one of the almost 400 million people with machine learning worldwide, This book offers a method to Techniques! Not every machine learning model uses the same techniques, so training will depend on your approach. Let's consider a few examples: Psychology of learning Machine learning in practice Reinforcement learning Types of machine learning Learning by reinforcement Types of reinforcement The different types of learning This guidebook is going to take some time to explore machine learning, and what it is all about. There are so many different aspects of machine learning and how to make it work for your needs, and all of it is found in this guidebook. Some of the different topics that you will be able to learn about inside include: Neural networks Historical background Why use neural networks? Tasks of neural networks Deep learning Algorithms Starting with python Basic types of data Get access to free software and data sets so you can try out your very own machine learning software. See how advanced machine learning will impact our world in the future! Scroll Up and Click the Buy Now Button!

Author : Kunal Miind
Publisher : BAKER & MCGILL
Page : 103 pages
File Size : 47,6 Mb
Release : 2022-01-02
Category : Computers
ISBN : 8210379456XXX

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AI SYSTEM BLUEPRINTS by Kunal Miind Pdf

"AI System Blueprints: An Introductory Guide to Building High Performance Artificial Intelligence & Machine Learning Solutions" by Kunal Miind is an exhaustive resource for those seeking a firm foundation in the ever-changing field of artificial intelligence and machine learning. The book begins with an introduction to the fundamentals of machine learning, including the various forms of learning, important algorithms, and fundamental concepts. The section then delves into vital data preparation steps such as collection, cleansing, and feature engineering. The following chapters discuss model selection and evaluation, deep learning fundamentals, natural language processing, and computer vision. The book also explores reinforcement learning's component parts and algorithms. By providing a clear and concise comprehension of these fundamental topics, Kunal Miind's book equips readers with the knowledge necessary to develop high-performance AI and machine learning solutions for practical applications.

Author : Gheorghe Tecuci,Yves Kodratoff
Publisher : Unknown
Page : 344 pages
File Size : 51,8 Mb
Release : 1995
Category : Business & Economics
ISBN : UOM:39015034522584

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Machine Learning and Knowledge Acquisition by Gheorghe Tecuci,Yves Kodratoff Pdf

Currently, both fields are moving towards an integrated approach using machine learning techniques to automate knowledge acquisition from experts, and knowledge acquisition techniques to guide and assist the learning process.

Author : Jon Adams
Publisher : Green Mountain Computing
Page : 117 pages
File Size : 42,6 Mb
Release : 2024-06-30
Category : Computers
ISBN : 8210379456XXX

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AI Foundations of Machine Learning by Jon Adams Pdf

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 : Rick Spair
Publisher : Rick Spair
Page : 462 pages
File Size : 45,7 Mb
Release : 2024-06-30
Category : Computers
ISBN : 9798223159728

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Mastering Machine Learning: A Comprehensive Guide to Success by Rick Spair Pdf

Welcome to "Mastering Machine Learning: A Comprehensive Guide to Success." In this book, we embark on an exciting journey into the world of machine learning (ML), exploring its concepts, techniques, and practical applications. Whether you are a beginner taking your first steps into the field or an experienced practitioner seeking to deepen your knowledge, this comprehensive guide will equip you with the tools, strategies, and insights needed to succeed in the ever-evolving landscape of ML. Machine learning is a rapidly advancing field that has revolutionized industries and transformed the way we tackle complex problems. From personalized recommendations and speech recognition systems to autonomous vehicles and medical diagnostics, machine learning has become an integral part of our daily lives. Its ability to analyze vast amounts of data, identify patterns, and make predictions has paved the way for groundbreaking advancements across various domains. However, mastering machine learning requires more than just understanding the algorithms and techniques. It requires a holistic approach that encompasses data collection and preparation, exploratory data analysis, model building, evaluation, deployment, and continuous learning. It also demands a deep understanding of the ethical and social implications of machine learning, ensuring responsible and fair use of this powerful technology. In this book, we have carefully crafted 20 comprehensive chapters that cover a wide range of topics, from the fundamentals of machine learning to advanced techniques and future trends. Each chapter provides a deep dive into a specific aspect of machine learning, offering tips, recommendations, and strategies for success. You will learn about various algorithms, data preprocessing techniques, model evaluation methods, interpretability approaches, and much more. Throughout the book, we emphasize a practical approach to machine learning. Real-world examples, case studies, and hands-on exercises are incorporated to help you gain a deeper understanding of the concepts and apply them to your own projects. We believe that active learning and practical experience are crucial for mastering machine learning, and we encourage you to explore, experiment, and build your own models. While this book serves as a comprehensive guide, it is important to note that machine learning is a rapidly evolving field. New algorithms, techniques, and technologies are constantly emerging, and staying up-to-date with the latest advancements is essential. However, the principles and foundations discussed in this book will provide you with a solid framework to adapt and navigate the ever-changing landscape of machine learning. Whether you are an aspiring data scientist, a software engineer, a researcher, or a business professional, this book is designed to be your trusted companion in your journey to mastering machine learning. By the time you reach the end, you will have gained a deep understanding of the fundamental concepts, acquired practical skills for applying machine learning in real-world scenarios, and developed the mindset needed to tackle complex challenges and drive innovation. Get ready to embark on an exciting adventure into the world of machine learning. Let's begin our journey towards mastering machine learning and unlocking its full potential. Happy learning!