Download or read book Learning with Uncertainty written by Xizhao Wang and published by CRC Press. This book was released on 2016-11-25 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion, and (4) Ensemble learning in a framework of uncertainty. The book starts with the introduction to uncertainty including randomness, roughness, fuzziness and non-specificity and then comprehensively discusses a number of key issues in learning with uncertainty, such as uncertainty representation in learning, the influence of uncertainty on the performance of learning system, the heuristic design with uncertainty, etc. Most contents of the book are our research results in recent decades. The purpose of this book is to help the readers to understand the impact of uncertainty on learning processes. It comes with many examples to facilitate understanding. The book can be used as reference book or textbook for researcher fellows, senior undergraduates and postgraduates majored in computer science and technology, applied mathematics, automation, electrical engineering, etc.
Download or read book Learning for Uncertainty written by G. Williamson McDiarmid and published by Routledge. This book was released on 2022-01-31 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning for Uncertainty explores technology’s role in education, specifically unpacking the question: How should educators prepare today’s children for a world that has yet to be made? As technology evolves faster than our capacity to fully understand the social, cultural, economic, and moral implications of many innovations, today’s educators are tasked with the unique role of preparing students to capitalize on technology’s opportunities and also mitigate its dangers to their society, to democratic processes, and to institutions. Veteran educators McDiarmid and Zhao explore the implications of emerging technologies for future jobs, organizations, students, and learning, covering topics such as • The future of work and workers as technology eliminates some industries while creating new ones. • Potential futures, both bright and dark, awaiting students. • The qualities, dispositions, social behaviors, and skills that are likely to advantage students in the future. • The possibility of technology to revolutionize education in ways that will better position students for an uncertain future. • How technology can free teachers from time and effort devoted to routine matters to instead assuming roles that are potentially more satisfying and supportive of their students’ learning. • Learning opportunities and educator roles that have the potential to bring about needed changes. • Capitalizing on these uncertain times to rethink curriculum, pedagogy, opportunities to learn, and the organization of school as well as the roles of students, educators, parents, and policymakers. This latest book in the Routledge Leading Change series is ideal reading for educators and policymakers in both P–12 and higher education interested in ensuring our education systems provide the experiences and learning opportunities necessary to cultivate the innovative, iconoclastic, and generative thinkers and creators needed for a future that nourishes the minds, bodies, and spirits of all.
Download or read book Computer Information Systems and Industrial Management written by Khalid Saeed and published by Springer. This book was released on 2013-09-20 with total page 541 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 12th IFIP TC 8 International Conference, CISIM 2013, held in Cracow, Poland, in September 2013. The 44 papers presented in this volume were carefully reviewed and selected from over 60 submissions. They are organized in topical sections on biometric and biomedical applications; pattern recognition and image processing; various aspects of computer security, networking, algorithms, and industrial applications. The book also contains full papers of a keynote speech and the invited talk.
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Download or read book Building Learning Capacity in an Age of Uncertainty written by James A. Bailey and published by Routledge. This book was released on 2021-05-19 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an increasingly complex context of global pandemic, economic uncertainty, increased racial inequities, and a climate crisis, this practical guide for school leaders explores how capacity as learning at the individual, team, and organizational level can help schools become more agile and adaptive. Author James A. Bailey unpacks a new model of capacity building that combines learning process, leadership mindsets, and skills to enhance learning. This research-based book includes a "Diagnostic for School Learning Capacity" and "Team Considerations" to help school leaders and their teams launch further discussions and advance learning in their contexts. The tools in this timely book are designed to help school leaders handle the increasing volatile, uncertain, complex, and ambiguous world in which schools now exist.
Download or read book Risky Teaching written by Jay W. Roberts and published by Routledge. This book was released on 2021-11-22 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risky Teaching examines the role of risk and uncertainty in teaching and learning in higher education. Discussing the current landscape of higher education and the challenges and opportunities we face, this book synthesizes a range of evidence-based and high-impact practices both in and out of the classroom, offering practical strategies and thought-provoking ideas on educational innovation for students and faculty. Covering topics such as taking risks inside the classroom, innovative teaching methods outside the classroom, rethinking assessment, and sustaining creativity as we grow in our careers, this practical resource is for faculty and instructors to work within and through uncertainty. The book also explores the inward challenges and opportunities associated with risky teaching and how institutional leaders can encourage productive risk-taking throughout the organization. This important text is for faculty and instructors in higher education who want to help their students thrive in a complex, unscripted, and disruptive world.
Download or read book Reckoning with Risk written by Gerd Gigerenzer and published by Penguin UK. This book was released on 2003-04-24 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are ordinary people able to reason with risk? Detailing case histories and examples, this text presents readers with tools for understanding statistics. In so doing, it encourages us to overcome our innumeracy and empowers us to take responsibility for our own choices.
Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Graphs in Biomedical Image Analysis written by Carole H. Sudre and published by Springer Nature. This book was released on 2020-10-05 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image Based Procedures written by Hayit Greenspan and published by Springer Nature. This book was released on 2019-10-10 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
Download or read book The Management of Uncertainty written by Angela Liberatore and published by Routledge. This book was released on 2013-10-18 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This investigative analysis studies why key European countries responded differently to the Chernobyl nuclear disaster, and what can be learned from it. The author details why the accident was defined differently in various countries, why actions were or were not taken, and what was learned about the management of nuclear risk. Furthermore, Liberatore studies the short-term and long-term responses and consequences of Chernobyl not only in specific countries, but within the European Union as a whole. Liberatore also provides a policy communication model to illustrate the interaction among the key personnel in such incidents: the scientists, the politicians, the interest groups, and the mass media. The author's focus upon uncertainty managementis a compelling account for all who seek to understand and improve the practical management of transboundary risks.
Download or read book Uncertainty written by Kostas Kampourakis and published by Oxford University Press, USA. This book was released on 2020 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anti-evolutionists, climate denialists, and anti-vaxxers, among others, question some of the best-established scientific findings by referring to the uncertainties in these areas of research. Uncertainty: How It Makes Science Advance shows that uncertainty is an inherent feature of science that makes it advance by motivating further research.
Download or read book Probability for Machine Learning written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-09-24 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.
Download or read book Controlling Uncertainty written by Magda Osman and published by John Wiley & Sons. This book was released on 2011-07-18 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Controlling Uncertainty: Decision Making and Learning in Complex Worlds reviews and discusses the most current research relating to the ways we can control the uncertain world around us. Features reviews and discussions of the most current research in a number of fields relevant to controlling uncertainty, such as psychology, neuroscience, computer science and engineering Presents a new framework that is designed to integrate a variety of disparate fields of research Represents the first book of its kind to provide a general overview of work related to understanding control
Download or read book EBOOK A Will to Learn Being a Student in an age of Uncertainty written by Ronald Barnett and published by McGraw-Hill Education (UK). This book was released on 2007-10-16 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an extraordinary but largely unnoticed phenomenon in higher education: by and large, students persevere and complete their studies. How should we interpret this tendency? Students are living in uncertain times and often experience anxiety, and yet they continue to press forward with their studies. The argument here is that we should understand this propensity on the part of students to persist through a will to learn. This book examines the structure of what it is to have a will to learn. Here, a language of being, becoming, authenticity, dispositions, voice, air, spirit, inspiration and care is drawn on. As such, this book offers an idea of student development that challenges the dominant views of our age, of curricula understood largely in terms of skill or even of knowledge, and pedagogy understood as bringing off pre-specified ‘outcomes’. The will to learn, though, can be fragile. This is of crucial importance, for if the will to learn dissolves, the student's commitment may falter. Accordingly, more than encouraging an interest in the student's subject or in the acquiring of skills, the primary responsibility of teachers in higher education is to sustain and develop the student's will to learn. This is a radical thesis, for it implies a transformation in how we understand the nature of teaching in higher education.
Download or read book The Uncertainty Mindset written by Vaughn Tan and published by Columbia University Press. This book was released on 2020-07-28 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Innovation is how businesses stay ahead of the competition and adapt to market conditions that change in unpredictable and uncertain ways. In the first decade of the twenty-first century, high-end cuisine underwent a profound transformation. Once an industry that prioritized consistency and reliability, it turned into one where constant change was a competitive necessity. A top restaurant’s reputation and success have become so closely bound up with its ability to innovate that a new organizational form, the culinary research and development team, has emerged. The best of these R&D teams continually expand the frontiers of food—they invent a constant stream of new dishes, new cooking processes and methods, and even new ways of experiencing food. How do they achieve this nonstop novelty? And what can culinary research and development teach us about how organizations innovate? Vaughn Tan opens up the black box of elite culinary R&D to provide essential insights. Drawing on years of unprecedented access to the best and most influential culinary R&D teams in the world, he reveals how they exemplify what he calls the uncertainty mindset. Such a mindset intentionally incorporates uncertainty into organization design rather than simply trying to reduce risk. It changes how organizations hire, set goals, and motivate team members and leads organizations to work in highly unconventional ways. A revelatory look at the R&D kitchen, The Uncertainty Mindset upends conventional wisdom about how to organize for innovation and offers practical insights for businesses trying to become innovative and adaptable.
Download or read book Uncertainty A Catalyst for Creativity Learning and Development written by Ronald A. Beghetto and published by Springer Nature. This book was released on 2022-07-18 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume brings together a group of international researchers and theorists from various intellectual and analytic traditions to explore the role uncertainty plays in creativity, learning, and development. Contributors to this volume draw on existing programs of research as well as introduce new and even speculative directions for research, theory and practice. Learning and life are filled with uncertainty. Although the experience of uncertainty can cause emotional discomfort or cognitive rigidity, uncertainty serves as a catalyst and condition for change. In this way, uncertainty represents a core facet in the interrelationship among creativity, learning, and development. Considerations for both the benefits and potential costs of uncertainty will be addressed in this volume with an aim of understanding how uncertainty can be better understood in light of creativity, learning, and development. Taken together this volume stands to contribute to our collective understanding of the role that uncertainty plays in learning and life and highlights how conceptualizing and studying uncertainty in new ways can promote positive and lasting change.
Download or read book Bayesian Learning for Neural Networks written by Radford M. Neal and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
