| cmu masters in data science: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| cmu masters in data science: Big Data-Enabled Nursing Connie W. Delaney, Charlotte A. Weaver, Judith J. Warren, Thomas R. Clancy, Roy L. Simpson, 2017-11-02 Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Health systems electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. This text reflects how the learning health system infrastructure is maturing, and being advanced by health information exchanges (HIEs) with multiple organizations blending their data, or enabling distributed computing. It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery. New opportunities for nursing and call for new skills in research methodologies are being further enabled by new partnerships spanning all sectors. |
| cmu masters in data science: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| cmu masters in data science: The Last Lecture Randy Pausch, Jeffrey Zaslow, 2010 The author, a computer science professor diagnosed with terminal cancer, explores his life, the lessons that he has learned, how he has worked to achieve his childhood dreams, and the effect of his diagnosis on him and his family. |
| cmu masters in data science: Business Trends in Practice Bernard Marr, 2021-11-15 WINNER OF THE BUSINESS BOOK OF THE YEAR AWARD 2022! Stay one step ahead of the competition with this expert review of the most impactful and disruptive business trends coming down the pike Far from slowing down, change and transformation in business seems to come only at a more and more furious rate. The last ten years alone have seen the introduction of groundbreaking new trends that pose new opportunities and challenges for leaders in all industries. In Business Trends in Practice: The 25+ Trends That Are Redefining Organizations, best-selling business author and strategist Bernard Marr breaks down the social and technological forces underlying these rapidly advancing changes and the impact of those changes on key industries. Critical consumer trends just emerging today—or poised to emerge tomorrow—are discussed, as are strategies for rethinking your organisation’s product and service delivery. The book also explores: Crucial business operations trends that are changing the way companies conduct themselves in the 21st century The practical insights and takeaways you can glean from technological and social innovation when you cut through the hype Disruptive new technologies, including AI, robotic and business process automation, remote work, as well as social and environmental sustainability trends Business Trends in Practice: The 25+ Trends That Are Redefining Organizations is a must-read resource for executives, business leaders and managers, and business development and innovation leads trying to get – and stay – on top of changes and disruptions that are right around the corner. |
| cmu masters in data science: 商业数据科学 刘建宏, 2021-12-31 【目录】 - 第一章 商业分析介绍 - 第二章 RapidMiner 介绍 - 第三章 RFM - 第四章 关联规则学习 Association Rules Learning - 第五章 支持向量机 SVM - 第六章 决策树 Decision Tree - 第七章 随机森林 Random Forest - 第八章 集成学习 AdaBoost - 第九章 集成学习 Bagging - 第十章 集成学习 Stacking - 第十一章 Split Validation & Cross Validation - 第十二章 K 均值算法 K-means - 第十三章 K 最近邻分类 kNN - 第十四章 朴素贝叶斯 Naive Bayes - 第十五章 线性回归模型 Linear Regression - 第十六章 逻辑蒂斯回归分析 Logistic Regression - 第十七章 经典判别分析 Discriminant Analysis |
| cmu masters in data science: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky Ning Wang, Genaro Rebolledo-Mendez, Vania Dimitrova, Noboru Matsuda, Olga C. Santos, 2023-06-29 This volume constitutes poster papers and late breaking results presented during the 24th International Conference on Artificial Intelligence in Education, AIED 2023, Tokyo, Japan, July 3–7, 2023. The 65 poster papers presented were carefully reviewed and selected from 311 submissions. This set of posters was complemented with the other poster contributions submitted for the Poster and Late Breaking results track of the AIED 2023 conference. |
| cmu masters in data science: Statistics in the Public Interest Alicia L. Carriquiry, Judith M. Tanur, William F. Eddy, 2022-04-22 This edited volume surveys a variety of topics in statistics and the social sciences in memory of the late Stephen Fienberg. The book collects submissions from a wide range of contemporary authors to explore the fields in which Fienberg made significant contributions, including contingency tables and log-linear models, privacy and confidentiality, forensics and the law, the decennial census and other surveys, the National Academies, Bayesian theory and methods, causal inference and causes of effects, mixed membership models, and computing and machine learning. Each section begins with an overview of Fienberg’s contributions and continues with chapters by Fienberg’s students, colleagues, and collaborators exploring recent advances and the current state of research on the topic. In addition, this volume includes a biographical introduction as well as a memorial concluding chapter comprised of entries from Stephen and Joyce Fienberg’s close friends, former students, colleagues, and other loved ones, as well as a photographic tribute. |
| cmu masters in data science: Large Scale and Big Data Sherif Sakr, Mohamed Gaber, 2014-06-25 Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques. |
| cmu masters in data science: Building Machine Learning Pipelines Hannes Hapke, Catherine Nelson, 2020-07-13 Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques |
| cmu masters in data science: All of Statistics Larry Wasserman, 2013-12-11 Taken literally, the title All of Statistics is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data. |
| cmu masters in data science: Principles of Robot Motion Howie Choset, Kevin M. Lynch, Seth Hutchinson, George A. Kantor, Wolfram Burgard, 2005-05-20 A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts. Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts. |
| cmu masters in data science: How Learning Works Susan A. Ambrose, Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, Marie K. Norman, 2010-04-16 Praise for How Learning Works How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning. —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching. —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues. —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book. —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning |
| cmu masters in data science: Managing and Processing Big Data in Cloud Computing Kannan, Rajkumar, 2016-01-07 Big data has presented a number of opportunities across industries. With these opportunities come a number of challenges associated with handling, analyzing, and storing large data sets. One solution to this challenge is cloud computing, which supports a massive storage and computation facility in order to accommodate big data processing. Managing and Processing Big Data in Cloud Computing explores the challenges of supporting big data processing and cloud-based platforms as a proposed solution. Emphasizing a number of crucial topics such as data analytics, wireless networks, mobile clouds, and machine learning, this publication meets the research needs of data analysts, IT professionals, researchers, graduate students, and educators in the areas of data science, computer programming, and IT development. |
| cmu masters in data science: Java Illuminated Julie Anderson, Hervé Franceschi, 2012 With a variety of interactive learning features and user-friendly pedagogy, the Third Edition provides a comprehensive introduction to programming using the most current version of Java. Throughout the text the authors incorporate an active learning approach which asks students to take an active role in their understanding of the language through the use of numerous interactive examples, exercises, and projects. Object-oriented programming concepts are developed progressively and reinforced through numerous Programming Activities, allowing students to fully understand and implement both basic and sophisticated techniques. In response to students growing interest in animation and visualization the text includes techniques for producing graphical output and animations beginning in Chapter 4 with applets and continuing throughout the text. You will find Java Illuminated, Third Edition comprehensive and user-friendly. Students will find it exciting to delve into the world of programming with hands-on, real-world applications!New to the Third Edition:-Includes NEW examples and projects throughout-Every NEW copy of the text includes a CD-ROM with the following: *programming activity framework code*full example code from each chapter*browser-based modules with visual step-by-step demonstrations of code execution*links to popular integrated development environments and the Java Standard Edition JDK-Every new copy includes full student access to TuringsCraft Custome CodeLab. Customized to match the organization of this textbook, CodeLab provides over 300 short hands-on programming exercises with immediate feedback.Instructor Resources: Test Bank, PowerPoint Lecture Outlines, Solutions to Programming Activities in text, and Answers to the chapter exercisesAlso available:Java Illuminated: Brief Edition, Third Edition (ISBN-13: 978-1-4496-3202-1). This Brief Edition is suitable for the one-term introductory course. |
| cmu masters in data science: Data Democracy Feras A. Batarseh, Ruixin Yang, 2020-01-21 Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering provides a manifesto to data democracy. After reading the chapters of this book, you are informed and suitably warned! You are already part of the data republic, and you (and all of us) need to ensure that our data fall in the right hands. Everything you click, buy, swipe, try, sell, drive, or fly is a data point. But who owns the data? At this point, not you! You do not even have access to most of it. The next best empire of our planet is one who owns and controls the world's best dataset. If you consume or create data, if you are a citizen of the data republic (willingly or grudgingly), and if you are interested in making a decision or finding the truth through data-driven analysis, this book is for you. A group of experts, academics, data science researchers, and industry practitioners gathered to write this manifesto about data democracy. - The future of the data republic, life within a data democracy, and our digital freedoms - An in-depth analysis of open science, open data, open source software, and their future challenges - A comprehensive review of data democracy's implications within domains such as: healthcare, space exploration, earth sciences, business, and psychology - The democratization of Artificial Intelligence (AI), and data issues such as: Bias, imbalance, context, and knowledge extraction - A systematic review of AI methods applied to software engineering problems |
| cmu masters in data science: Stochastic Calculus for Finance I Steven Shreve, 2005-06-28 Developed for the professional Master's program in Computational Finance at Carnegie Mellon, the leading financial engineering program in the U.S. Has been tested in the classroom and revised over a period of several years Exercises conclude every chapter; some of these extend the theory while others are drawn from practical problems in quantitative finance |
| cmu masters in data science: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| cmu masters in data science: Machine Learning Jaime Guillermo Carbonell, 1989 |
| cmu masters in data science: Integer Programming Michele Conforti, Gérard Cornuéjols, Giacomo Zambelli, 2014-11-15 This book is an elegant and rigorous presentation of integer programming, exposing the subject’s mathematical depth and broad applicability. Special attention is given to the theory behind the algorithms used in state-of-the-art solvers. An abundance of concrete examples and exercises of both theoretical and real-world interest explore the wide range of applications and ramifications of the theory. Each chapter is accompanied by an expertly informed guide to the literature and special topics, rounding out the reader’s understanding and serving as a gateway to deeper study. Key topics include: formulations polyhedral theory cutting planes decomposition enumeration semidefinite relaxations Written by renowned experts in integer programming and combinatorial optimization, Integer Programming is destined to become an essential text in the field. |
| cmu masters in data science: Encyclopedia of Cloud Computing San Murugesan, Irena Bojanova, 2016-08-01 The Encyclopedia of Cloud Computing provides IT professionals, educators, researchers and students with a compendium of cloud computing knowledge. Authored by a spectrum of subject matter experts in industry and academia, this unique publication, in a single volume, covers a wide range of cloud computing topics, including technological trends and developments, research opportunities, best practices, standards, and cloud adoption. Providing multiple perspectives, it also addresses questions that stakeholders might have in the context of development, operation, management, and use of clouds. Furthermore, it examines cloud computing's impact now and in the future. The encyclopedia presents 56 chapters logically organized into 10 sections. Each chapter covers a major topic/area with cross-references to other chapters and contains tables, illustrations, side-bars as appropriate. Furthermore, each chapter presents its summary at the beginning and backend material, references and additional resources for further information. |
| cmu masters in data science: Foundations of Machine Learning, second edition Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 2018-12-25 A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. |
| cmu masters in data science: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| cmu masters in data science: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| cmu masters in data science: The Creativity Code Marcus Du Sautoy, 2020-03-03 “A brilliant travel guide to the coming world of AI.” —Jeanette Winterson What does it mean to be creative? Can creativity be trained? Is it uniquely human, or could AI be considered creative? Mathematical genius and exuberant polymath Marcus du Sautoy plunges us into the world of artificial intelligence and algorithmic learning in this essential guide to the future of creativity. He considers the role of pattern and imitation in the creative process and sets out to investigate the programs and programmers—from Deep Mind and the Flow Machine to Botnik and WHIM—who are seeking to rival or surpass human innovation in gaming, music, art, and language. A thrilling tour of the landscape of invention, The Creativity Code explores the new face of creativity and the mysteries of the human code. “As machines outsmart us in ever more domains, we can at least comfort ourselves that one area will remain sacrosanct and uncomputable: human creativity. Or can we?...In his fascinating exploration of the nature of creativity, Marcus du Sautoy questions many of those assumptions.” —Financial Times “Fascinating...If all the experiences, hopes, dreams, visions, lusts, loves, and hatreds that shape the human imagination amount to nothing more than a ‘code,’ then sooner or later a machine will crack it. Indeed, du Sautoy assembles an eclectic array of evidence to show how that’s happening even now.” —The Times |
| cmu masters in data science: Analysis of Boolean Functions Ryan O'Donnell, 2014-06-05 This graduate-level text gives a thorough overview of the analysis of Boolean functions, beginning with the most basic definitions and proceeding to advanced topics. |
| cmu masters in data science: Proceedings of the international conference on Machine Learning John Anderson, E. R. Bareiss, Ryszard Stanisław Michalski, 19?? |
| cmu masters in data science: Smart Education and e-Learning - Smart Pedagogy Vladimir L. Uskov, Robert J. Howlett, Lakhmi C. Jain, 2022-05-28 This book serves as a reference for researchers and practitioners in academia and industry. Smart education, smart e-learning and smart pedagogy are emerging and rapidly growing areas that have a potential to transform existing teaching strategies, learning environments and educational activities and technology. They are focused at enabling instructors to develop innovative ways of achieving excellence in teaching in highly technological smart university and providing students with new opportunities to maximize their success using smart classrooms, smart systems and technology. This book contains the contributions presented at the 9th international KES conference on Smart Education and e-Learning (SEEL-2022) with the Smart Pedagogy as the main conference theme. It comprises of forty nine high-quality peer-reviewed papers that are grouped into several interconnected parts: Part 1—Smart Pedagogy, Part 2—Smart Education, Part 3—Smart e-Learning, Part 4—Smart University, Part 5—Smart Education: Systems and Technology, Part 6—Digital Humanities and Social Sciences for Smart University Development: the Innovative Methods, Models and Technologies, Part 7—Digital Transformation of Education and Economics in Smart University and Part 8—Smart Education for Children with Special Educational Needs. We believe this book will serve as a useful source of research data and valuable information for faculty, scholars, Ph.D. students, administrators and practitioners—those who are interested in smart education, smart e-learning and smart pedagogy. |
| cmu masters in data science: Scientific and Technical Aerospace Reports , 1995 |
| cmu masters in data science: Capital Ideas Evolving Peter L. Bernstein, 2011-01-31 A lot has happened in the financial markets since 1992, when Peter Bernstein wrote his seminal Capital Ideas. Happily, Peter has taken up his facile pen again to describe these changes, a virtual revolution in the practice of investing that relies heavily on complex mathematics, derivatives, hedging, and hyperactive trading. This fine and eminently readable book is unlikely to be surpassed as the definitive chronicle of a truly historic era. John C. Bogle, founder of The Vanguard Group and author, The Little Book of Common Sense Investing Just as Dante could not have understood or survived the perils of the Inferno without Virgil to guide him, investors today need Peter Bernstein to help find their way across dark and shifting ground. No one alive understands Wall Street's intellectual history better, and that makes Bernstein our best and wisest guide to the future. He is the only person who could have written this book; thank goodness he did. Jason Zweig, Investing Columnist, Money magazine Another must-read from Peter Bernstein! This well-written and thought-provoking book provides valuable insights on how key finance theories have evolved from their ivory tower formulation to profitable application by portfolio managers. This book will certainly be read with keen interest by, and undoubtedly influence, a wide range of participants in international finance. Dr. Mohamed A. El-Erian, President and CEO of Harvard Management Company, Deputy Treasurer of Harvard University, and member of the faculty of the Harvard Business School Reading Capital Ideas Evolving is an experience not to be missed. Peter Bernstein's knowledge of the principal characters-the giants in the development of investment theory and practice-brings this subject to life. Linda B. Strumpf, Vice President and Chief Investment Officer, The Ford Foundation With great clarity, Peter Bernstein introduces us to the insights of investment giants, and explains how they transformed financial theory into portfolio practice. This is not just a tale of money and models; it is a fascinating and contemporary story about people and the power of their ideas. Elroy Dimson, BGI Professor of Investment Management, London Business School Capital Ideas Evolving provides us with a unique appreciation for the pervasive impact that the theory of modern finance has had on the development of our capital markets. Peter Bernstein once again has produced a masterpiece that is must reading for practitioners, educators and students of finance. Andr F. Perold, Professor of Finance, Harvard Business School |
| cmu masters in data science: Learning Machine Translation Cyril Goutte, Nicola Cancedda, Marc Dymetman, George Foster, 2009 How Machine Learning can improve machine translation: enabling technologies and new statistical techniques. |
| cmu masters in data science: Embedded Robotics Thomas Bräunl, 2008-09-20 This book presents a unique examination of mobile robots and embedded systems, from introductory to intermediate level. It is structured in three parts, dealing with Embedded Systems (hardware and software design, actuators, sensors, PID control, multitasking), Mobile Robot Design (driving, balancing, walking, and flying robots), and Mobile Robot Applications (mapping, robot soccer, genetic algorithms, neural networks, behavior-based systems, and simulation). The book is written as a text for courses in computer science, computer engineering, IT, electronic engineering, and mechatronics, as well as a guide for robot hobbyists and researchers. |
| cmu masters in data science: Computer Science Robert Sedgewick, Kevin Wayne, 2016-06-17 Named a Notable Book in the 21st Annual Best of Computing list by the ACM! Robert Sedgewick and Kevin Wayne’s Computer Science: An Interdisciplinary Approach is the ideal modern introduction to computer science with Java programming for both students and professionals. Taking a broad, applications-based approach, Sedgewick and Wayne teach through important examples from science, mathematics, engineering, finance, and commercial computing. The book demystifies computation, explains its intellectual underpinnings, and covers the essential elements of programming and computational problem solving in today’s environments. The authors begin by introducing basic programming elements such as variables, conditionals, loops, arrays, and I/O. Next, they turn to functions, introducing key modular programming concepts, including components and reuse. They present a modern introduction to object-oriented programming, covering current programming paradigms and approaches to data abstraction. Building on this foundation, Sedgewick and Wayne widen their focus to the broader discipline of computer science. They introduce classical sorting and searching algorithms, fundamental data structures and their application, and scientific techniques for assessing an implementation’s performance. Using abstract models, readers learn to answer basic questions about computation, gaining insight for practical application. Finally, the authors show how machine architecture links the theory of computing to real computers, and to the field’s history and evolution. For each concept, the authors present all the information readers need to build confidence, together with examples that solve intriguing problems. Each chapter contains question-and-answer sections, self-study drills, and challenging problems that demand creative solutions. Companion web site (introcs.cs.princeton.edu/java) contains Extensive supplementary information, including suggested approaches to programming assignments, checklists, and FAQs Graphics and sound libraries Links to program code and test data Solutions to selected exercises Chapter summaries Detailed instructions for installing a Java programming environment Detailed problem sets and projects Companion 20-part series of video lectures is available at informit.com/title/9780134493831 |
| cmu masters in data science: Optimization Methods in Finance Gerard Cornuejols, Reha Tütüncü, 2006-12-21 Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses. |
| cmu masters in data science: e-Learning and the Science of Instruction Ruth C. Clark, Richard E. Mayer, 2016-03-21 The essential e-learning design manual, updated with the latest research, design principles, and examples e-Learning and the Science of Instruction is the ultimate handbook for evidence-based e-learning design. Since the first edition of this book, e-learning has grown to account for at least 40% of all training delivery media. However, digital courses often fail to reach their potential for learning effectiveness and efficiency. This guide provides research-based guidelines on how best to present content with text, graphics, and audio as well as the conditions under which those guidelines are most effective. This updated fourth edition describes the guidelines, psychology, and applications for ways to improve learning through personalization techniques, coherence, animations, and a new chapter on evidence-based game design. The chapter on the Cognitive Theory of Multimedia Learning introduces three forms of cognitive load which are revisited throughout each chapter as the psychological basis for chapter principles. A new chapter on engagement in learning lays the groundwork for in-depth reviews of how to leverage worked examples, practice, online collaboration, and learner control to optimize learning. The updated instructor's materials include a syllabus, assignments, storyboard projects, and test items that you can adapt to your own course schedule and students. Co-authored by the most productive instructional research scientist in the world, Dr. Richard E. Mayer, this book distills copious e-learning research into a practical manual for improving learning through optimal design and delivery. Get up to date on the latest e-learning research Adopt best practices for communicating information effectively Use evidence-based techniques to engage your learners Replace popular instructional ideas, such as learning styles with evidence-based guidelines Apply evidence-based design techniques to optimize learning games e-Learning continues to grow as an alternative or adjunct to the classroom, and correspondingly, has become a focus among researchers in learning-related fields. New findings from research laboratories can inform the design and development of e-learning. However, much of this research published in technical journals is inaccessible to those who actually design e-learning material. By collecting the latest evidence into a single volume and translating the theoretical into the practical, e-Learning and the Science of Instruction has become an essential resource for consumers and designers of multimedia learning. |
| cmu masters in data science: The Complete Guide to Capital Markets for Quantitative Professionals Alex Kuznetsov, 2006-11-22 The Complete Guide to Capital Markets for Quantitative Professionals is a comprehensive resource for readers with a background in science and technology who want to transfer their skills to the financial industry. It is written in a clear, conversational style and requires no prior knowledge of either finance or financial analytics. The book begins by discussing the operation of the financial industry and the business models of different types of Wall Street firms, as well as the job roles those with technical backgrounds can fill in those firms. Then it describes the mechanics of how these firms make money trading the main financial markets (focusing on fixed income, but also covering equity, options and derivatives markets), and highlights the ways in which quantitative professionals can participate in this money-making process. The second half focuses on the main areas of Wall Street technology and explains how financial models and systems are created, implemented, and used in real life. This is one of the few books that offers a review of relevant literature and Internet resources. |
| cmu masters in data science: Practical Deep Learning for Cloud, Mobile, and Edge Anirudh Koul, Siddha Ganju, Meher Kasam, 2019-10-14 Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users |
| cmu masters in data science: Interactions , 1997 A magazine for designers of interactive products. |
| cmu masters in data science: Towards Trustworthy Artificial Intelligent Systems Maria Isabel Aldinhas Ferreira, Mohammad Osman Tokhi, 2022-09-07 This present book provides valuable insights on the technical, societal and legal challenges posed by the use of artificial intelligent systems in a plethora of different applications, from embodied robotic systems to ML algorithms. Engaging with concerns about equity, privacy, surveillance and respect for human dignity, “Towards Trustworthy Artificial Intelligent Systems” highlights the fundamental factors on which stakeholders’ trust relies, identifying benchmarking, standardisation and certification as milestones grounding and consolidating that future trust. The multidisciplinary approach followed will make this book a valuable resource for all those involved in the production and deployment of AIs, as well as for academia and legal practitioners. |
| cmu masters in data science: Kicking Butt in Computer Science Carol Frieze, Jeria Quesenberry, 2015-11-30 Are women really kicking butt in computer science? National statistics show little progress in the participation of women in computing; this in spite of numerous studies, reports and recommendations on the topic. Some might say the reasons for the situation remain a mystery. However, at Carnegie Mellon University we do not believe that the situation is either so mysterious or such an intractable problem. Indeed, women are kicking butt in computer science in some cultures and environments. This book tells the Carnegie Mellon story, a positive story of how one school developed a culture and environment in which both women and men could thrive and be successful in computer science. |
在卡内基梅隆大学（CMU）学习计算机是怎样的体验？ - 知乎
我印象最深的是CMU浓厚的编程语言的研究氛围。 这在其他的名校里是很少见的。 你看School of Computer Science历任主任里， Peter Lee, Randy Bryant, 以及Computer Science Department …
申请cmu需要什么水平？ - 知乎
从官网信息中易知cmu有很多cs的硕士项目，cmu的特点就是这20多个项目总有一款适合你，从录取率上来说项目难度从top 4 - top 30 不等。具体的录取率可以从官网下载。 cmu的计算机学 …
在卡耐基梅隆大学学习是怎样一种体验？ - 知乎
a2: cmu在匹兹堡市属于大学城的区域，离市中心较远，但周围吃住很方便，也很安全。大范围来讲，匹兹堡地处东海岸，大部分时间是春秋的温度，离纽约和华盛顿比较近，喜欢玩的同学周 …
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cmu-sv是卡耐基梅隆大学于2002年在硅谷开设的一个新校区，地理位置可谓得天独厚，就业率也是非常高的。 除了地理位置外， SV校区与主校区相比，教学资源相对弱一些，课程设置比较偏 …
在卡内基梅隆大学 (Carnegie Mellon University) 就读是怎样一番体 …
cmu是一个全民cs的地方，如果你在别的学校，你会看到EE的人问转cs难不难，在cmu，连学英语的人都转学cs了，所以这个问题永远不存在。 cmu提供了这种氛围，就是无论你过去如何，来 …
在卡内基梅隆（CMU）念本科是什么样的体验？ - 知乎
cmu本校区的学习氛围非常好，周围的同学都非常认真，当然这和cmu繁重的课业压力有着很大的关系。 对于我来说，研究生期间和本科相比简直是太累了，CMU的课程设置和上课节奏逼着 …
CMU 15445/15645 Database Systems 存活指北 - 知乎
May 28, 2023 · CMU排行榜里有两个同学把这一个指标也从10+提升到了7, 8万. 根据他们的说法, 他们是自己手动track了这些Count而不是每次都去真的运行那个SQL Query, 从而降低了资源 …
CMU 计算机选课上有什么特别好的课程推荐？ - 知乎
在cmu一年半的学习生涯中收获了很多，同时也走了一些弯路，所以写下这篇课程介绍的文章，供学弟学妹们参考。 我以下介绍的这些课都是我自己实际上过的，并且都拿了A的，每门课包括 …
美国四大CS名校CMU的计算机课程介绍（内附官方教程链接）
May 19, 2022 · CMU 计算机科学专业 | hackway.org. 卡内基梅隆大学CMU号称自己是世界上第一个开设计算机专业的学校（开设于1965年）。当我翻开计算机科学史时又有说是普渡大 …
CMU有哪些值得一上的算法课？ - 知乎
（大多数课没有video，（CMU印象里好像有录像的大多是AI的课？）不过大多数课有lecture note，会把课上来龙去脉详细的讲出来。没有来美国的同学们，考虑到大家的英语水平，其实 …
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7 Updated 10/2020 Notes: ♦Statistics include special, non-degree summer programs, which are mainly Pre-College, Dalcroze and EMBA students. These were added beginning in M/F 06. …
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consulting, marketing, data science, and other sectors. The department offers undergraduate majors in Behavioral Economics, in Decision Science, and in Policy and Management.
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egocentric human data. Prior approaches that use human data for reward learning either require a small sample of in-domain robot data in training or need a goal image specified in the robot's …
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Data Structures and Algorithms Solving Recurrence Relations Chris Brooks Department of Computer Science University of San Francisco Department of Computer Science — University …
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45-821 – Internet Marketing, MBA Program, CMU Graduate School of Industrial Administration, Spring 2002. 70-488 – Internet Marketing, CMU Graduate School of Industrial Administration, …
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Research Statement Jimeng Sun (jimeng@cs.cmu.edu) My primary research interests are in the flelds of data mining, machine learning, and database systems. My research focuses on the …
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Being in the CMU School of Computer Science (SCS) is a unique experience because of the size of the school (over 2000 students, staff and faculty), the quality of its members, the variety of …
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The Language Technologies Institute offers one Ph.D. program and five Masters’ degree programs. • Ph.D. in Language and Information Technologies (LTI Ph.D.) • Masters in …
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to data. This component does not have to contain novel results; instead, its goal is to characterize how well or poorly the tools perform for the given data.) Students are encouraged to seek out …
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Travis D. Breaux 5 Organizing Co-Chair, 7th International Workshop on Requirements Engineering and Law (RELAW’14), co-located with RE’14, Karlskrona, Sweden, September …
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2.4 Data Providers and Software Vendors All nancial institutions depend on data. There are rms that specialize in providing this data, and in addition to data they generally provide nancial …
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dimensional data is a challenging problem and it gives the opportunity for statisticians to develop new methodologies to address it.” 2.What’s the story? Demonstrate the trajectory of your …
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The Fifth-Year Masters Program is a research-oriented Masters degree program that only accepts students with a BS in Computer Science from Carnegie Mellon. One of the primary motivations …
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That’s why the CMU Rales Fellows program was created: to empower you and connect you with others who share your passion, and to advance the next generation of STEM leaders like you. …
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sifies all the data correctly is shown in Figure 1. We will accept other possible solutions since decision boundary depends on the value of C(it is possible for the trained classifier to miss …
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training data is getting bigger and bigger [13], and the models are becoming deeper and deeper [7]. The scaling in both training data and model complexity provides both opportunities and …
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VI. Bachelor of Science Option Students may elect to earn a Bachelor of Science rather than a Bachelor of Arts degree by completing two courses from the list below, or by petitioning the …
S T U D E N T H A N D B O O K 2020-2021 - mcds.cs.cmu.edu
The Master of Computational Data Science (MCDS) degree is a professional Master of Science degree offered by the Language Technologies Institute (LTI), a department in the School of ...
