Download or read book Optimizing Data to Learning to Action written by Steven Flinn and published by Apress. This book was released on 2018-04-06 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply a powerful new approach and method that ensures continuous performance improvement for your business. You will learn how to determine and value the people, process, and technology-based solutions that will optimize your organization’s data-to-learning-to-action processes. This book describes in detail how to holistically optimize the chain of activities that span from data to learning to decisions to actions, an imperative for achieving outstanding performance in today’s business environment. Adapting and integrating insights from decision science, constraint theory, and process improvement, the book provides a method that is clear, effective, and can be applied to nearly every business function and sector. You will learn how to systematically work backwards from decisions to data, estimate the flow of value along the chain, and identify the inevitable value bottlenecks. And, importantly, you will learn techniques for quantifying the value that can be attained by successfully addressing the bottlenecks, providing the credible support needed to make the right level of investments at the right place and at just the right time. In today’s dynamic environment, with its never-ending stream of new, disruptive technologies that executives must consider (e.g., cloud computing, Internet of Things, AI/machine learning, business intelligence, enterprise social, etc., along with the associated big data generated), author Steven Flinn provides the comprehensive approach that is needed for making effective decisions about these technologies, underpinned by credibly quantified value. What You’ll Learn Understand data-to-learning-to-action processes and their fundamental elements Discover the highest leverage data-to-learning-to-action processes in your organization Identify the key decisions that are associated with a data-to-learning-to-action process Know why it’s NOT all about data, but it IS all about decisions and learning Determine the value upside of enhanced learning that can improve decisions Work backwards from the decisions to determine the value constraints in data-to-learning-to-action processes Evaluate people, process, and technology-based solution options to address the constraints Quantify the expected value of each of the solution options and prioritize accordingly Implement, measure, and continuously improve by addressing the next constraints on value Who This Book Is For Business executives and managers seeking the next level of organizational performance, knowledge workers who want to maximize their impact, technology managers and practitioners who require a more effective means to prioritize technology options and deployments, technology providers who need a way to credibly quantify the value of their offerings, and consultants who are ready to build practices around the next big business performance paradigm

Download or read book Optimizing Data to learning to action written by Steven Flinn and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply a powerful new approach and method that ensures continuous performance improvement for your business. You will learn how to determine and value the people, process, and technology-based solutions that will optimize your organization's data-to-learning-to-action processes. This book describes in detail how to holistically optimize the chain of activities that span from data to learning to decisions to actions, an imperative for achieving outstanding performance in today's business environment. Adapting and integrating insights from decision science, constraint theory, and process improvement, the book provides a method that is clear, effective, and can be applied to nearly every business function and sector. You will learn how to systematically work backwards from decisions to data, estimate the flow of value along the chain, and identify the inevitable value bottlenecks. And, importantly, you will learn techniques for quantifying the value that can be attained by successfully addressing the bottlenecks, providing the credible support needed to make the right level of investments at the right place and at just the right time. In today's dynamic environment, with its never-ending stream of new, disruptive technologies that executives must consider (e.g., cloud computing, Internet of Things, AI/machine learning, business intelligence, enterprise social, etc., along with the associated big data generated), author Steven Flinn provides the comprehensive approach that is needed for making effective decisions about these technologies, underpinned by credibly quantified value. What You'll Learn: Understand data-to-learning-to-action processes and their fundamental elements Discover the highest leverage data-to-learning-to-action processes in your organization Identify the key decisions that are associated with a data-to-learning-to-action process Know why it's NOT all about data, but it IS all about decisions and learning Determine the value upside of enhanced learning that can improve decisions Work backwards from the decisions to determine the value constraints in data-to-learning-to-action processes Evaluate people, process, and technology-based solution options to address the constraints Quantify the expected value of each of the solution options and prioritize accordingly Implement, measure, and continuously improve by addressing the next constraints on value.

Download or read book Optimizing the Power of Action Learning written by Michael J. Marquardt and published by Hachette UK. This book was released on 2018-06-14 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Third Edition of the field-defining book Originated by Reg Revans in the 1940s, the Action Learning Model was refined and then reintroduced by lead author Michael Marquardt to organizations globally as a powerful tool for improving organizational performance. Today, Marquardt is widely considered to be the modern "father" of the Action Learning approach. For this new edition, Marquardt has teamed up with three Action Learning experts from Asia and the UK who bring a broader global approach to what has become THE seminal book in the field. Each chapter has been updated for alignment with today's practice and implementation of Action Learning in organizations, including fresh information on virtual Action Learning, guidance on implementing Action Learning and becoming a Certified Action Learning Coach, and many new case studies.

Download or read book Machine Learning Optimization and Big Data written by Panos M. Pardalos and published by Springer. This book was released on 2016-12-24 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the Second International Workshop on Machine Learning, Optimization, and Big Data, MOD 2016, held in Volterra, Italy, in August 2016. The 40 papers presented in this volume were carefully reviewed and selected from 97 submissions. These proceedings contain papers in the fields of Machine Learning, Computational Optimization and DataScience presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Download or read book Machine Learning Optimization and Data Science written by Giuseppe Nicosia and published by Springer Nature. This book was released on 2020-01-03 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Download or read book Optimization for Data Analysis written by Stephen J. Wright and published by Cambridge University Press. This book was released on 2022-04-21 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Download or read book Data Driven Evolutionary Optimization written by Yaochu Jin and published by Springer Nature. This book was released on 2021-06-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.

Download or read book Artificial Intelligence for Business Optimization written by Bhuvan Unhelkar and published by CRC Press. This book was released on 2021-08-09 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains how AI and Machine Learning can be applied to help businesses solve problems, support critical thinking and ultimately create customer value and increase profit. By considering business strategies, business process modeling, quality assurance, cybersecurity, governance and big data and focusing on functions, processes, and people’s behaviors it helps businesses take a truly holistic approach to business optimization. It contains practical examples that make it easy to understand the concepts and apply them. It is written for practitioners (consultants, senior executives, decision-makers) dealing with real-life business problems on a daily basis, who are keen to develop systematic strategies for the application of AI/ML/BD technologies to business automation and optimization, as well as researchers who want to explore the industrial applications of AI and higher-level students.

Download or read book Patterns Predictions and Actions Foundations of Machine Learning written by Moritz Hardt and published by Princeton University Press. This book was released on 2022-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers

Download or read book The Elements of Joint Learning and Optimization in Operations Management written by Xi Chen and published by Springer Nature. This book was released on 2022-09-20 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.

Download or read book Machine Learning Optimization and Big Data written by Panos M. Pardalos and published by Springer. This book was released on 2016-12-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the Second International Workshop on Machine Learning, Optimization, and Big Data, MOD 2016, held in Volterra, Italy, in August 2016. The 40 papers presented in this volume were carefully reviewed and selected from 97 submissions. These proceedings contain papers in the fields of Machine Learning, Computational Optimization and DataScience presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Download or read book Automated Machine Learning in Action written by Qingquan Song and published by Simon and Schuster. This book was released on 2022-06-07 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. --

Download or read book Design and Optimization for 5G Wireless Communications written by Haesik Kim and published by John Wiley & Sons. This book was released on 2020-06-02 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a technical background to the design and optimization of wireless communication systems, covering optimization algorithms for wireless and 5G communication systems design. The book introduces the design and optimization systems which target capacity, latency, and connection density; including Enhanced Mobile Broadband Communication (eMBB), Ultra-Reliable and Low Latency Communication (URLL), and Massive Machine Type Communication (mMTC). The book is organized into two distinct parts: Part I, mathematical methods and optimization algorithms for wireless communications are introduced, providing the reader with the required mathematical background. In Part II, 5G communication systems are designed and optimized using the mathematical methods and optimization algorithms.

Download or read book Machine Learning Optimization and Big Data written by Giuseppe Nicosia and published by Springer. This book was released on 2017-12-19 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the Third International Workshop on Machine Learning, Optimization, and Big Data, MOD 2017, held in Volterra, Italy, in September 2017. The 50 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Download or read book Intelligent Computing Optimization written by Pandian Vasant and published by Springer Nature. This book was released on 2022-10-20 with total page 1215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of Springer Nature is another proof of Springer’s outstanding and greatness on the lively interface of Smart Computational Optimization, Green ICT, Smart Intelligence and Machine Learning! It is a Master Piece of what our community of academics and experts can provide when an Interconnected Approach of Joint, Mutual and Meta Learning is supported by Modern Operational Research and Experience of the World-Leader Springer Nature! The 5th edition of International Conference on Intelligent Computing and Optimization took place at October 27–28, 2022, via Zoom. Objective was to celebrate “Creativity with Compassion and Wisdom” with researchers, scholars, experts and investigators in Intelligent Computing and Optimization across the planet, to share knowledge, experience, innovation—a marvelous opportunity for discourse and mutuality by novel research, invention and creativity. This proceedings book of ICO’2022 is published by Springer Nature—Quality Label of wonderful.

Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

Download or read book Learning and Intelligent Optimization written by Roberto Battiti and published by Springer. This book was released on 2018-12-31 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Conference on Learning and Intelligent Optimization, LION 12, held in Kalamata, Greece, in June 2018. The 28 full papers and 12 short papers presented have been carefully reviewed and selected from 62 submissions. The papers explore the advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning.