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Author: Schahin Tofangchi
Publisher: Cuvillier Verlag
ISBN: 3736962002
Category : Business & Economics
Languages : en
Pages : 202
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Book Description
The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable.
Author: Schahin Tofangchi
Publisher: Cuvillier Verlag
ISBN: 3736962002
Category : Business & Economics
Languages : en
Pages : 202
Get Book Here
Book Description
The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable.
Author: Chip Huyen
Publisher: "O'Reilly Media, Inc."
ISBN: 1098107918
Category : Computers
Languages : en
Pages : 387
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Book Description
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Author: Sasha Costanza-Chock
Publisher: MIT Press
ISBN: 0262043459
Category : Design
Languages : en
Pages : 358
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Book Description
An exploration of how design might be led by marginalized communities, dismantle structural inequality, and advance collective liberation and ecological survival. What is the relationship between design, power, and social justice? “Design justice” is an approach to design that is led by marginalized communities and that aims expilcitly to challenge, rather than reproduce, structural inequalities. It has emerged from a growing community of designers in various fields who work closely with social movements and community-based organizations around the world. This book explores the theory and practice of design justice, demonstrates how universalist design principles and practices erase certain groups of people—specifically, those who are intersectionally disadvantaged or multiply burdened under the matrix of domination (white supremacist heteropatriarchy, ableism, capitalism, and settler colonialism)—and invites readers to “build a better world, a world where many worlds fit; linked worlds of collective liberation and ecological sustainability.” Along the way, the book documents a multitude of real-world community-led design practices, each grounded in a particular social movement. Design Justice goes beyond recent calls for design for good, user-centered design, and employment diversity in the technology and design professions; it connects design to larger struggles for collective liberation and ecological survival.
Author: Ariel Geib
Publisher: Springer Nature
ISBN: 3031015789
Category : Computers
Languages : en
Pages : 134
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Book Description
Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.
Author: William F. Lawless
Publisher: Springer Nature
ISBN: 3030772837
Category : Computers
Languages : en
Pages : 566
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Book Description
This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.
Author: Tanner Fiez
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
As a result of the demonstrated potential for impact in traditional use cases, progressively more is being asked of machine learning methods. This evolution has lead to a renewed focus on learning and decision-making systems. In this domain, theoretical challenges relating to competition and uncertainty are emerging from the practical considerations that have motivated this paradigm shift. There is an increasing awareness that learning and decision-making algorithms will eventually need to be or already are being embedded into complex systems where game-theoretic considerations naturally arise owing to the presence of competing, self-interested entities. Moreover, it has become clear that the artificial introduction of competition in game-theoretic abstractions of machine learning problems can often be a convenient and effective modeling technique for many problems of interest. Consequently, tools from game theory are now critically needed to analyze coupled learning and decision-making algorithms for the purposes of characterizing the outcomes that can be expected from competitive interactions and computing meaningful solutions such as equilibria in machine learning problems. Meanwhile, the demands of learning and decision-making algorithms operating under uncertainty are both changing and becoming more challenging. This transformation includes a movement towards more general, yet structured feedback models and objectives that reflect the desire to enable downstream tasks and future inferences. To this end, important problems remain to be solved pertaining to designing theoretically sound sequential decision-making algorithms tailored to such tasks. This discussion motivates the research on learning and decision-making in competitive and uncertain systems presented in this thesis. Together, the contents of this thesis can be summarized by a pair of themes that form Parts I and II: game-theoretic methods for analyzing decision-making algorithms and solving machine learning problems, and machine learning methods for designing and analyzing sequential decision-making algorithms under uncertainty. The former theme is approached from a top-down perspective: general formulations of games and gradient-based learning algorithms are studied, theoretical characterizations are developed, and then the results are connected to specific problems of interest. In contrast, the latter theme is approached from a bottom-up perspective: models of practical sequential decision-making tasks are developed and then theoretically justified algorithms and solutions are constructed. While learning and optimization in games is a well-studied topic, the majority of past research has focused on highly structured settings. Part I of this thesis moves away from this practice and presents studies of nonconvex games on continuous strategy spaces and gradient-based learning algorithms within them. The intent of this research is to develop appropriate notions of game-theoretic equilibria, characterize and understand the behaviors of so-called `natural' learning dynamics, and establish methods for computing equilibria to solve machine learning problems formulated as games. Chapter 2 lays the foundation for Part I and is built upon thereafter. Based upon the idea of viewing the underlying interaction structure as a Stackelberg game, both a local Stackelberg equilibrium concept and a corresponding characterization in terms of gradient-based sufficient conditions called a differential Stackelberg equilibrium are presented. Learning dynamics emulating the natural game structure are then constructed and convergence guarantees to differential Stackelberg equilibrium are proven. Chapter 3 follows along this path to study the role of timescale separation on the convergence of the canonical gradient descent-ascent learning dynamics in the subclass of nonconvex-nonconcave zero-sum games. The results characterize the timescales for which the dynamics both locally converge to differential Stackelberg equilibrium and locally avoid points lacking game-theoretic meaning. Finally, Chapter 4 considers zero-sum games in which the minimizing player faces a nonconvex objective and the maximizing player optimizes a Polyak-Lojasiewicz or strongly-concave objective. For this class of games, global convergence guarantees for gradient descent-ascent with timescale separation to only differential Stackelberg equilibrium are proven. Throughout Part I, the implications of the theoretical results for both competitive decision-making and methods for solving machine learning problems are discussed. Traditionally, the study of sequential decision-making under uncertainty in machine learning has focused on problems in which the evaluation criterion is directly linked to the immediate feedback. However, it has become clear that decision-making under uncertainty is often also pertinent to problems where the goal of the learner is instead to acquire information for the purpose of drawing inferences or fulfilling targets only partially linked to the immediate feedback. Part II of this thesis presents a pair of studies on well-motivated sequential decision-making problems with structured feedback models that fall under this theme. The intent of this research is to design sequential decision-making algorithms for solving practical problems that emerge in the real-world with desirable theoretical guarantees by exploiting structured feedback models. Chapter 5 commences Part II by formulating the task of ranking papers to reviewers in peer review bidding systems as a sequential decision-making problem. A model of this problem is developed that identifies a pair of misaligned objectives: ensuring that each paper obtains a sufficient number of bids to be matched adequately with qualified reviewers, and respecting the preferences of reviewers by showing them relevant papers early in the list. To balance the competing objectives, a sequential decision-making algorithm is constructed that exploits the objective structure and it is shown both theoretically and empirically to have a number of advantages over baselines currently used in practice.Chapter 6 then concludes Part II with an analysis of pure exploration transductive linear bandits, a problem that arises naturally in experimental design settings. A decision-maker in this problem sequentially samples measurement vectors from a given set and observes a noisy linear response with an unknown parameter vector. The goal is to infer with high confidence the item from a separate set of vectors that has the maximum inner product with the unknown parameter vector while taking a minimal number of measurements. The optimal achievable sample complexity for this problem is characterized and a near-optimal algorithm that exploits the information structure of the feedback model to enhance the sample efficiency is developed. Together, the contributions of this thesis take steps towards developing important theoretical foundations for learning and decision-making with competition and uncertainty.
Author: Jitendra Kumar Rout
Publisher: Springer Nature
ISBN: 9811536899
Category : Technology & Engineering
Languages : en
Pages : 219
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Book Description
The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.
Author: Parag Kulkarni
Publisher: John Wiley & Sons
ISBN: 1118271556
Category : Technology & Engineering
Languages : en
Pages : 324
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Book Description
Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making. Chapters include: Introduction to Reinforcement and Systemic Machine Learning Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning Systemic Machine Learning and Model Inference and Information Integration Adaptive Learning Incremental Learning and Knowledge Representation Knowledge Augmentation: A Machine Learning Perspective Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Author: Nhut T. M. Vo
Publisher:
ISBN: 9781032635088
Category : Computers
Languages : en
Pages : 0
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Book Description
As Multi-Criteria Decision-Making (MCDM) continues to grow and evolve, Machine Learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems. This book is intended to guide researchers, practitioners, and students interested in the intersection of ML and MCDM for optimal design. Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is a comprehensive resource that bridges the gap between ML and MCDM. It offers a practical approach by demonstrating the application of ML and MCDM algorithms to real-world problems. Through case studies and examples, the book showcases the effectiveness of these techniques in optimal design. By providing a comparative analysis of conventional MCDM algorithms and machine learning techniques, the readers are able to make informed decisions about their use in different scenarios. The book also explores emerging trends, providing insights into future directions and potential opportunities. A wide range of topics are covered including the definition of optimal design, MCDM algorithms, supervised and unsupervised ML techniques, deep learning techniques, and more, making it a valuable resource for professionals and researchers in various fields. Designed for professionals, researchers, and practitioners in engineering, computer science, sustainability, and related fields, the book is also a valuable resource for students and academics who wish to expand their knowledge of machine learning applications in multi-criteria decision-making. By offering a blend of theoretical insights and practical examples, this guide aims to inspire further research and application of machine learning in multidimensional decision-making environments.
