Download or read book Veridical Data Science written by Bin Yu and published by MIT Press. This book was released on 2024-10-15 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven results Teaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process Cultivates critical thinking throughout the entire data science life cycle Provides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions Suitable for advanced undergraduate and graduate students, domain scientists, and practitioners

Download or read book Leadership in Statistics and Data Science written by Amanda L. Golbeck and published by Springer Nature. This book was released on 2021-03-22 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited collection brings together voices of the strongest thought leaders on diversity, equity and inclusion in the field of statistics and data science, with the goal of encouraging and steering the profession into the regular practice of inclusive and humanistic leadership. It provides futuristic ideas for promoting opportunities for equitable leadership, as well as tested approaches that have already been found to make a difference. It speaks to the challenges and opportunities of leading successful research collaborations and making strong connections within research teams. Curated with a vision that leadership takes a myriad of forms, and that diversity has many dimensions, this volume examines the nuances of leadership within a workplace environment and promotes storytelling and other competencies as critical elements of effective leadership. It makes the case for inclusive and humanistic leadership in statistics and data science, where there often remains a dearth of women and members of certain racial communities among the employees. Titled and non-titled leaders will benefit from the planning, evaluation, and structural tools offered within to contribute inclusive excellence in workplace climate, environment, and culture.

Download or read book CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS written by Giovanni C. Porzio and published by Firenze University Press. This book was released on with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research.

Download or read book Guide to Intelligent Data Analysis written by Michael R. Berthold and published by Springer. This book was released on 2010-07-01 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.

Download or read book Science Reason and Faith written by Fr. Robert Spitzer, SJ and published by Our Sunday Visitor. This book was released on 2023-09-18 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Built into our very nature is a desire to know about the world around us. The big questions of human existence are inescapable: Who am I? Why am I here, and where am I going? Why is there evil in the world? What is the meaning of life? This yearning for truth ultimately leads us to our Creator. God knows the longings of the human heart, and he reveals himself to us through creation, through Scripture, and ultimately through the Incarnation. Because God the Son became man, we have a person to look to in our pursuit of truth: Jesus Christ himself, who is Truth. Christ helps us see that truth is not just the object of science and reason, but what animates the mysterious and loving power of faith. In Science, Reason, and Faith, Fr. Robert Spitzer, SJ, explores in depth the Bible and the intersection of three realms that the secular world tells us are separate and incompatible. Fr. Spitzer draws the modern reader's attention to the many seeming conflicts between science, reason, and Catholic teaching. By tackling these difficult questions, he shows that it is precisely through the integration of science, reason, and faith that we can truly discover ourselves, our world, and our God.

Download or read book Communicating with Data written by Deborah Nolan and published by Oxford University Press, USA. This book was released on 2021-03 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data

Download or read book Veridical Data Science written by Bin Yu and published by MIT Press. This book was released on 2024-10-15 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven results Teaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process Cultivates critical thinking throughout the entire data science life cycle Provides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions Suitable for advanced undergraduate and graduate students, domain scientists, and practitioners

Download or read book ECKM 2023 24th European Conference on Knowledge Management Vol 2 written by Alvaro Rosa and published by Academic Conferences and publishing limited. This book was released on 2023-09-07 with total page 834 pages. Available in PDF, EPUB and Kindle. Book excerpt: These proceedings represent the work of contributors to the 24th European Conference on Knowledge Management (ECKM 2023), hosted by Iscte – Instituto Universitário de Lisboa, Portugal on 7-8 September 2023. The Conference Chair is Prof Florinda Matos, and the Programme Chair is Prof Álvaro Rosa, both from Iscte Business School, Iscte – Instituto Universitário de Lisboa, Portugal. ECKM is now a well-established event on the academic research calendar and now in its 24th year the key aim remains the opportunity for participants to share ideas and meet the people who hold them. The scope of papers will ensure an interesting two days. The subjects covered illustrate the wide range of topics that fall into this important and ever-growing area of research. The opening keynote presentation is given by Professor Leif Edvinsson, on the topic of Intellectual Capital as a Missed Value. The second day of the conference will open with an address by Professor Noboru Konno from Tama Graduate School and Keio University, Japan who will talk about Society 5.0, Knowledge and Conceptual Capability, and Professor Jay Liebowitz, who will talk about Digital Transformation for the University of the Future. With an initial submission of 350 abstracts, after the double blind, peer review process there are 184 Academic research papers, 11 PhD research papers, 1 Masters Research paper, 4 Non-Academic papers and 11 work-in-progress papers published in these Conference Proceedings. These papers represent research from Australia, Austria, Brazil, Bulgaria, Canada, Chile, China, Colombia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, India, Iran, Iraq, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Kuwait, Latvia, Lithuania, Malaysia, México, Morocco, Netherlands, Norway, Palestine, Peru, Philippines, Poland, Portugal, Romania, South Africa, Spain, Sweden, Switzerland, Taiwan, Thailand, Tunisia, UK, United Arab Emirates and the USA.

Download or read book High Performance Big Data Computing written by Dhabaleswar K. Panda and published by MIT Press. This book was released on 2022-08-02 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.

Download or read book Data Science and Classification written by Vladimir Batagelj and published by Springer Science & Business Media. This book was released on 2006-09-05 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science and Classification provides new methodological developments in data analysis and classification. The broad and comprehensive coverage includes the measurement of similarity and dissimilarity, methods for classification and clustering, network and graph analyses, analysis of symbolic data, and web mining. Beyond structural and theoretical results, the book offers application advice for a variety of problems, in medicine, microarray analysis, social network structures, and music.

Download or read book Knowledge Graphs written by Mayank Kejriwal and published by MIT Press. This book was released on 2021-03-30 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence. The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.

Download or read book Computability and Complexity written by Neil D. Jones and published by MIT Press. This book was released on 1997 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computability and complexity theory should be of central concern to practitioners as well as theorists. Unfortunately, however, the field is known for its impenetrability. Neil Jones's goal as an educator and author is to build a bridge between computability and complexity theory and other areas of computer science, especially programming. In a shift away from the Turing machine- and G�del number-oriented classical approaches, Jones uses concepts familiar from programming languages to make computability and complexity more accessible to computer scientists and more applicable to practical programming problems. According to Jones, the fields of computability and complexity theory, as well as programming languages and semantics, have a great deal to offer each other. Computability and complexity theory have a breadth, depth, and generality not often seen in programming languages. The programming language community, meanwhile, has a firm grasp of algorithm design, presentation, and implementation. In addition, programming languages sometimes provide computational models that are more realistic in certain crucial aspects than traditional models. New results in the book include a proof that constant time factors do matter for its programming-oriented model of computation. (In contrast, Turing machines have a counterintuitive "constant speedup" property: that almost any program can be made to run faster, by any amount. Its proof involves techniques irrelevant to practice.) Further results include simple characterizations in programming terms of the central complexity classes PTIME and LOGSPACE, and a new approach to complete problems for NLOGSPACE, PTIME, NPTIME, and PSPACE, uniformly based on Boolean programs. Foundations of Computing series

Download or read book Science at the Doorstep to God written by Robert Spitzer and published by Ignatius Press. This book was released on 2023-10-23 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, there is more science-based evidence for God, the soul, and life after death than ever before. Then why are scores of people turning to unbelief because of "science"? The answer is simple: they do not know the science. Science at the Doorstep to God presents in depth the latest evidence to turn the rising tide of unbelief. Father Robert Spitzer, S.J., synthesizes eight recent studies confirming an intelligent creator of physical reality as well as a transphysical soul capable of surviving bodily death. This is the most comprehensive scientific treatment of God and the afterlife to date. It combines natural scientific method, metaphysical method, medical studies, anthropological and genetics studies, and phenomenological descriptions, showing how each distinct method and data set reinforces the others. It is critical for the Church to learn and share the fruits of this research and again to demonstrate the profound complementarity between the Catholic faith and science. Through reason, we can come to see not only the great intelligence of the Creator, but also signs of his love, goodness, and glory.

Download or read book Computational Limitations of Small depth Circuits written by Johan Håstad and published by MIT Press (MA). This book was released on 1987 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proving lower bounds on the amount of resources needed to compute specific functions is one of the most active branches of theoretical computer science. Significant progress has been made recently in proving lower bounds in two restricted models of Boolean circuits. One is the model of small depth circuits, and in this book Johan Torkel Hastad has developed very powerful techniques for proving exponential lower bounds on the size of small depth circuits' computing functions. The techniques described in Computational Limitations for Small Depth Circuitscan be used to demonstrate almost optimal lower bounds on the size of small depth circuits computing several different functions, such as parity and majority. The main tool used in the proof of the lower bounds is a lemma, stating that any AND of small fanout OR gates can be converted into an OR of small fanout AND gates with high probability when random values are substituted for the variables. Hastad also applies this tool to relativized complexity, and discusses in great detail the computation of parity and majority in small depth circuits. Contents:Introduction. Small Depth Circuits. Outline of Lower Bound Proofs. Main Lemma. Lower Bounds for Small Depth Circuits. Functions Requiring Depth k to Have Small Circuits. Applications to Relativized Complexity. How Well Can We Compute Parity in Small Depth? Is Majority Harder than Parity? Conclusions. John Hastad is a postdoctoral fellow in the Department of Mathematics at MIT Computational Limitations of Small Depth Circuitsis a winner of the 1986 ACM Doctoral Dissertation Award.

Download or read book Introduction to Natural Language Processing written by Jacob Eisenstein and published by MIT Press. This book was released on 2019-10-01 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.

Download or read book Active Inference written by Thomas Parr and published by MIT Press. This book was released on 2022-03-29 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.

Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.