| computer science berkeley requirements: Structure and Interpretation of Computer Programs Harold Abelson, Gerald Jay Sussman, 2022-05-03 A new version of the classic and widely used text adapted for the JavaScript programming language. Since the publication of its first edition in 1984 and its second edition in 1996, Structure and Interpretation of Computer Programs (SICP) has influenced computer science curricula around the world. Widely adopted as a textbook, the book has its origins in a popular entry-level computer science course taught by Harold Abelson and Gerald Jay Sussman at MIT. SICP introduces the reader to central ideas of computation by establishing a series of mental models for computation. Earlier editions used the programming language Scheme in their program examples. This new version of the second edition has been adapted for JavaScript. The first three chapters of SICP cover programming concepts that are common to all modern high-level programming languages. Chapters four and five, which used Scheme to formulate language processors for Scheme, required significant revision. Chapter four offers new material, in particular an introduction to the notion of program parsing. The evaluator and compiler in chapter five introduce a subtle stack discipline to support return statements (a prominent feature of statement-oriented languages) without sacrificing tail recursion. The JavaScript programs included in the book run in any implementation of the language that complies with the ECMAScript 2020 specification, using the JavaScript package sicp provided by the MIT Press website. |
| computer science berkeley requirements: Optimization Models Giuseppe C. Calafiore, Laurent El Ghaoui, 2014-10-31 This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects. |
| computer science berkeley requirements: Raspberry Pi User Guide Eben Upton, Gareth Halfacree, 2016-08-29 Learn the Raspberry Pi 3 from the experts! Raspberry Pi User Guide, 4th Edition is the unofficial official guide to everything Raspberry Pi 3. Written by the Pi's creator and a leading Pi guru, this book goes straight to the source to bring you the ultimate Raspberry Pi 3 manual. This new fourth edition has been updated to cover the Raspberry Pi 3 board and software, with detailed discussion on its wide array of configurations, languages, and applications. You'll learn how to take full advantage of the mighty Pi's full capabilities, and then expand those capabilities even more with add-on technologies. You'll write productivity and multimedia programs, and learn flexible programming languages that allow you to shape your Raspberry Pi into whatever you want it to be. If you're ready to jump right in, this book gets you started with clear, step-by-step instruction from software installation to system customization. The Raspberry Pi's tremendous popularity has spawned an entire industry of add-ons, parts, hacks, ideas, and inventions. The movement is growing, and pushing the boundaries of possibility along with it—are you ready to be a part of it? This book is your ideal companion for claiming your piece of the Pi. Get all set up with software, and connect to other devices Understand Linux System Admin nomenclature and conventions Write your own programs using Python and Scratch Extend the Pi's capabilities with add-ons like Wi-Fi dongles, a touch screen, and more The credit-card sized Raspberry Pi has become a global phenomenon. Created by the Raspberry Pi Foundation to get kids interested in programming, this tiny computer kick-started a movement of tinkerers, thinkers, experimenters, and inventors. Where will your Raspberry Pi 3 take you? The Raspberry Pi User Guide, 3rd Edition is your ultimate roadmap to discovery. |
| computer science berkeley requirements: How to Be a High School Superstar Cal Newport, 2010-07-27 Do Less, Live More, Get Accepted What if getting into your reach schools didn’t require four years of excessive A.P. classes, overwhelming activity schedules, and constant stress? In How to Be a High School Superstar, Cal Newport explores the world of relaxed superstars—students who scored spots at the nation’s top colleges by leading uncluttered, low stress, and authentic lives. Drawing from extensive interviews and cutting-edge science, Newport explains the surprising truths behind these superstars’ mixture of happiness and admissions success, including: · Why doing less is the foundation for becoming more impressive. · Why demonstrating passion is meaningless, but being interesting is crucial. · Why accomplishments that are hard to explain are better than accomplishments that are hard to do. These insights are accompanied by step-by-step instructions to help any student adopt the relaxed superstar lifestyle—proving that getting into college doesn’t have to be a chore to survive, but instead can be the reward for living a genuinely interesting life. |
| computer science berkeley requirements: Law and Policy for the Quantum Age Chris Jay Hoofnagle, Simson L. Garfinkel, 2022-01-06 The Quantum Age cuts through the hype to demystify quantum technologies, their development paths, and the policy issues they raise. |
| computer science berkeley requirements: 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. |
| computer science berkeley requirements: Molecular Environmental Biology Seymour J. Garte, 1993-11-23 Molecular Environmental Biology is the first book to illustrate molecular biological approaches to major issues in environmental biology. International experts have contributed representative chapters that cover how molecular methods and concepts apply to wildlife management, ecology, pollution control and remediation, and environmental health. Specific topics discussed include the use of molecular techniques in the population biology of wild animals and in the management of fisheries, bioremediation, cloning and characterization of the genes responsible for degradation of PCBs and related environmental pollutants, molecular analysis of aromatic hydrocarbon degradation by soil bacteria, and molecular biological techniques in assessing environmental damage to natural habitats. The book also explores how new molecular approaches can be applied to human disease etiology and epidemiology. Topics discussed in this area include an introduction to molecular epidemiology, the uses of molecular biological markers in cancer risk assessment, specific environmental carcinogens found in foods, measuring DNA adducts and mutation frequencies to assess environmental toxic exposures and effect, and using the extent of gene inducibility as a dosimeter of toxic exposure. This book will interest researchers and students in all fields of environmental biology and environmental medicine. Readers will find information on new techniques and applications of established molecular methodology that will stimulate new research ideas, collaborations, and progress. Researchers will now have a chance to make rapid progress on environmental questions that were previously not even open for exploration. |
| computer science berkeley requirements: Introduction to the Theory of Computation Michael Sipser, 2012-06-27 Now you can clearly present even the most complex computational theory topics to your students with Sipser’s distinct, market-leading INTRODUCTION TO THE THEORY OF COMPUTATION, 3E. The number one choice for today’s computational theory course, this highly anticipated revision retains the unmatched clarity and thorough coverage that make it a leading text for upper-level undergraduate and introductory graduate students. This edition continues author Michael Sipser’s well-known, approachable style with timely revisions, additional exercises, and more memorable examples in key areas. A new first-of-its-kind theoretical treatment of deterministic context-free languages is ideal for a better understanding of parsing and LR(k) grammars. This edition’s refined presentation ensures a trusted accuracy and clarity that make the challenging study of computational theory accessible and intuitive to students while maintaining the subject’s rigor and formalism. Readers gain a solid understanding of the fundamental mathematical properties of computer hardware, software, and applications with a blend of practical and philosophical coverage and mathematical treatments, including advanced theorems and proofs. INTRODUCTION TO THE THEORY OF COMPUTATION, 3E’s comprehensive coverage makes this an ideal ongoing reference tool for those studying theoretical computing. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| computer science berkeley requirements: Mathematical Thinking and Problem Solving Alan H. Schoenfeld, Alan H. Sloane, 2016-05-06 In the early 1980s there was virtually no serious communication among the various groups that contribute to mathematics education -- mathematicians, mathematics educators, classroom teachers, and cognitive scientists. Members of these groups came from different traditions, had different perspectives, and rarely gathered in the same place to discuss issues of common interest. Part of the problem was that there was no common ground for the discussions -- given the disparate traditions and perspectives. As one way of addressing this problem, the Sloan Foundation funded two conferences in the mid-1980s, bringing together members of the different communities in a ground clearing effort, designed to establish a base for communication. In those conferences, interdisciplinary teams reviewed major topic areas and put together distillations of what was known about them.* A more recent conference -- upon which this volume is based -- offered a forum in which various people involved in education reform would present their work, and members of the broad communities gathered would comment on it. The focus was primarily on college mathematics, informed by developments in K-12 mathematics. The main issues of the conference were mathematical thinking and problem solving. |
| computer science berkeley requirements: Open Sources Chris DiBona, Sam Ockman, 1999-01-03 Freely available source code, with contributions from thousands of programmers around the world: this is the spirit of the software revolution known as Open Source. Open Source has grabbed the computer industry's attention. Netscape has opened the source code to Mozilla; IBM supports Apache; major database vendors haved ported their products to Linux. As enterprises realize the power of the open-source development model, Open Source is becoming a viable mainstream alternative to commercial software.Now in Open Sources, leaders of Open Source come together for the first time to discuss the new vision of the software industry they have created. The essays in this volume offer insight into how the Open Source movement works, why it succeeds, and where it is going.For programmers who have labored on open-source projects, Open Sources is the new gospel: a powerful vision from the movement's spiritual leaders. For businesses integrating open-source software into their enterprise, Open Sources reveals the mysteries of how open development builds better software, and how businesses can leverage freely available software for a competitive business advantage.The contributors here have been the leaders in the open-source arena: Brian Behlendorf (Apache) Kirk McKusick (Berkeley Unix) Tim O'Reilly (Publisher, O'Reilly & Associates) Bruce Perens (Debian Project, Open Source Initiative) Tom Paquin and Jim Hamerly (mozilla.org, Netscape) Eric Raymond (Open Source Initiative) Richard Stallman (GNU, Free Software Foundation, Emacs) Michael Tiemann (Cygnus Solutions) Linus Torvalds (Linux) Paul Vixie (Bind) Larry Wall (Perl) This book explains why the majority of the Internet's servers use open- source technologies for everything from the operating system to Web serving and email. Key technology products developed with open-source software have overtaken and surpassed the commercial efforts of billion dollar companies like Microsoft and IBM to dominate software markets. Learn the inside story of what led Netscape to decide to release its source code using the open-source mode. Learn how Cygnus Solutions builds the world's best compilers by sharing the source code. Learn why venture capitalists are eagerly watching Red Hat Software, a company that gives its key product -- Linux -- away.For the first time in print, this book presents the story of the open- source phenomenon told by the people who created this movement.Open Sources will bring you into the world of free software and show you the revolution. |
| computer science berkeley requirements: Berkeley UNIX James Wilson, 1991-01-16 This comprehensive, one-semester introduction to Unix, used at Stanford University, incorporates sound pedagogy along with all the necessary reference material. Begins with the basic commands and finishes with advanced programming techniques. Offers strong coverage of systems calls and contains an excellent problem set. |
| computer science berkeley requirements: Engineering Software as a Service Armando Fox, David A. Patterson, 2016 (NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details.(NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details. |
| computer science berkeley requirements: Designation of Lawrence Berkeley Laboratory Computer Facility as a Federal Scientific Data Processing Center Could Save Millions United States. General Accounting Office, 1976 |
| computer science berkeley requirements: Algorithms Sanjoy Dasgupta, Christos H. Papadimitriou, Umesh Virkumar Vazirani, 2006 This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. Features include:The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated. Carefully chosen advanced topics that can be skipped in a standard one-semester course but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text DasGupta also offers a Solutions Manual which is available on the Online Learning Center.Algorithms is an outstanding undergraduate text equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel it is a joy to read. Tim Roughgarden Stanford University |
| computer science berkeley requirements: 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. |
| computer science berkeley requirements: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
| computer science berkeley requirements: HT THINK LIKE A COMPUTER SCIEN Jeffrey Elkner, Allen B. Downey, Chris Meyers, 2016-10-04 The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions. The single most important skill for a computer scientist is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That's why this chapter is called, The way of the program. On one level, you will be learning to program, a useful skill by itself. On another level, you will use programming as a means to an end. As we go along, that end will become clearer. |
| computer science berkeley requirements: GRE Prep by Magoosh Magoosh, Chris Lele, Mike McGarry, 2016-12-07 Magoosh gives students everything they need to make studying a breeze. We've branched out from our online GRE prep program and free apps to bring you this GRE prep book. We know sometimes you don't have easy access to the Internet--or maybe you just like scribbling your notes in the margins of a page! Whatever your reason for picking up this book, we're thrilled to take this ride together. In these pages you'll find: --Tons of tips, FAQs, and GRE strategies to get you ready for the big test. --More than 130 verbal and quantitative practice questions with thorough explanations. --Stats for each practice question, including its difficulty rating and the percent of students who typically answer it correctly. We want you to know exactly how tough GRE questions tend to be so you'll know what to expect on test day. --A full-length practice test with an answer key and detailed explanations. --Multiple practice prompts for the analytical writing assessment section, with tips on how to grade each of your essays. If you're not already familiar with Magoosh online, here's what you need to know: --Our materials are top-notch--we've designed each of our practice questions based on careful analysis of millions of students' answers. --We really want to see you do your best. That's why we offer a score improvement guarantee to students who use the online premium Magoosh program. --20% of our students earn a top 10% score on the GRE. --Magoosh students score on average 12 points higher on the test than all other GRE takers. --We've helped more than 1.5 million students prepare for standardized tests online and with our mobile apps. So crack open this book, join us online at magoosh.com, and let's get you ready to rock the GRE! |
| computer science berkeley requirements: Random Graph Dynamics Rick Durrett, 2010-05-31 The theory of random graphs began in the late 1950s in several papers by Erdos and Renyi. In the late twentieth century, the notion of six degrees of separation, meaning that any two people on the planet can be connected by a short chain of people who know each other, inspired Strogatz and Watts to define the small world random graph in which each site is connected to k close neighbors, but also has long-range connections. At a similar time, it was observed in human social and sexual networks and on the Internet that the number of neighbors of an individual or computer has a power law distribution. This inspired Barabasi and Albert to define the preferential attachment model, which has these properties. These two papers have led to an explosion of research. The purpose of this book is to use a wide variety of mathematical argument to obtain insights into the properties of these graphs. A unique feature is the interest in the dynamics of process taking place on the graph in addition to their geometric properties, such as connectedness and diameter. |
| computer science berkeley requirements: Berkeley DB Sleepycat Software Inc, 2001 Small, special-purpose computing devices and high-end core Internet servers need fast, reliable database management. Berkeley DB is an embedded database that provides high-performance, scalable, transaction-protected and recoverable data management services to applications. Extremely portable, this library runs under almost all UNIX and Windows variants, as well as a number of embedded, real-time operating systems. Berkeley DB is the ultimate resource for the world's most widely deployed embedded database engine. This book will aid software architects and engineers, product managers, and systems and network administrators without the overhead imposed by other database products. Designed by programmers for programmers, this classic library style toolkit provides a broad base of functionality to application writers. This book will help you to make intelligent choices about when and how to use Berkeley DB to meet your needs. You can visit the Sleepycat website to get the latest errata for this book. NOTE: The first printing of this book contained an error in the table of contents that caused the page numbers to be off. This will be corrected in the second printing. If you have an earlier edition, you can download a pdf of the correct table of contents that you can print out and use with your book. If you have any questions, please feel free to contact the editor of this book at stephanie.wall@newriders.com. |
| computer science berkeley requirements: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
| computer science berkeley requirements: Mathematics for Computer Science Eric Lehman, F. Thomson Leighton, Albert R. Meyer, 2017-03-08 This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions. |
| computer science berkeley requirements: The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2013-11-11 During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
| computer science berkeley requirements: Graduate Programs in Engineering & Applied Sciences 2011 (Grad 5) Peterson's, 2011-05-01 Peterson's Graduate Programs in Engineering & Applied Sciences contains a wealth of information on colleges and universities that offer graduate degrees in the fields of Aerospace/Aeronautical Engineering; Agricultural Engineering & Bioengineering; Architectural Engineering, Biomedical Engineering & Biotechnology; Chemical Engineering; Civil & Environmental Engineering; Computer Science & Information Technology; Electrical & Computer Engineering; Energy & Power engineering; Engineering Design; Engineering Physics; Geological, Mineral/Mining, and Petroleum Engineering; Industrial Engineering; Management of Engineering & Technology; Materials Sciences & Engineering; Mechanical Engineering & Mechanics; Ocean Engineering; Paper & Textile Engineering; and Telecommunications. Up-to-date data, collected through Peterson's Annual Survey of Graduate and Professional Institutions, provides valuable information on degree offerings, professional accreditation, jointly offered degrees, part-time and evening/weekend programs, postbaccalaureate distance degrees, faculty, students, degree requirements, entrance requirements, expenses, financial support, faculty research, and unit head and application contact information. As an added bonus, readers will find a helpful See Close-Up link to in-depth program descriptions written by some of these institutions. These Close-Ups offer detailed information about the specific program or department, faculty members and their research, and links to the program Web site. In addition, there are valuable articles on financial assistance and support at the graduate level and the graduate admissions process, with special advice for international and minority students. Another article discusses important facts about accreditation and provides a current list of accrediting agencies. |
| computer science berkeley requirements: Neural Networks: Tricks of the Trade Grégoire Montavon, Geneviève Orr, Klaus-Robert Müller, 2012-11-14 The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems. |
| computer science berkeley requirements: Simply Scheme Brian Harvey, Matthew Wright, 1999 Showing off scheme - Functions - Expressions - Defining your own procedures - Words and sentences - True and false - Variables - Higher-order functions - Lambda - Introduction to recursion - The leap of faith - How recursion works - Common patterns in recursive procedures - Advanced recursion - Example : the functions program - Files - Vectors - Example : a spreadsheet program - Implementing the spreadsheet program - What's next? |
| computer science berkeley requirements: Integer Linear Programming in Computational and Systems Biology Dan Gusfield, 2019-06-13 This hands-on tutorial text for non-experts demonstrates biological applications of a versatile modeling and optimization technique. |
| computer science berkeley requirements: Ambient Intelligence Werner Weber, Jan Rabaey, Emile H.L. Aarts, 2005-03-04 Ambient intelligence is the vision of a technology that will become invisibly embedded in our natural surroundings, present whenever we need it, enabled by simple and effortless interactions, attuned to all our senses, adaptive to users and context-sensitive, and autonomous. High-quality information access and personalized content must be available to everybody, anywhere, and at any time. This book addresses ambient intelligence used to support human contacts and accompany an individual's path through the complicated modern world. From the technical standpoint, distributed electronic intelligence is addressed as hardware vanishing into the background. Devices used for ambient intelligence are small, low-power, low weight, and (very importantly) low-cost; they collaborate or interact with each other; and they are redundant and error-tolerant. This means that the failure of one device will not cause failure of the whole system. Since wired connections often do not exist, radio methods will play an important role for data transfer. This book addresses various aspects of ambient intelligence, from applications that are imminent since they use essentially existing technologies, to ambitious ideas whose realization is still far away, due to major unsolved technical challenges. |
| computer science berkeley requirements: Encyclopedia of Computer Science and Technology Harry Henderson, 2009 Presents an illustrated A-Z encyclopedia containing approximately 600 entries on computer and technology related topics. |
| computer science berkeley requirements: SOFSEM 2006: Theory and Practice of Computer Science Jirí Wiedermann, 2006-01-05 This book constitutes the refereed proceedings of the 32nd Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2006, held in Merin, Czech Republic in January 2006. The 45 revised full papers, including the best Student Research Forum paper, presented together with 10 invited contributions were carefully reviewed and selected from 157 submissions. The papers were organized in four topical tracks on computer science foundations, wireless, mobile, ad hoc and sensor networks, database technologies, and semantic Web technologies. |
| computer science berkeley requirements: Distributed Applications and Interoperable Systems Frank Eliassen, Rüdiger Kapitza, 2010-05-10 The scope of LNCS, including its subseries LNAI and LNBI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application field. --Book Jacket. |
| computer science berkeley requirements: University Bulletin University of California (System), 1982 |
| computer science berkeley requirements: Semi-Supervised Learning Olivier Chapelle, Bernhard Scholkopf, Alexander Zien, 2010-01-22 A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction. |
| computer science berkeley requirements: Future Directions in Distributed Computing André Schiper, Alex A. Shvartsman, Hakim Weatherspoon, Ben Y. Zhao, 2003-07-01 Every year we witness acceleration in the availability, deployment, and use of distributed applications. However building increasingly sophisticated applications for extant and emerging networked systems continues to be challenging for several reasons: – Abstract models of computation used in distributed systems research often do not fully capture the limitations and the unpredictable nature of realistic distributed computing platforms; – Fault-tolerance and ef?ciency of computation are dif?cult to combine when the c- puting medium is subject to changes, asynchrony, and failures; – Middleware used for constructing distributed software does not provide services most suitable for sophisticated distributed applications; – Middleware services are speci?ed informally and without precise guarantees of e- ciency, fault-tolerance, scalability, and compositionality; – Speci?cation of distributed deployment of software systems is often left out of the development process; – Finally, there persists an organizational and cultural gap between engineering groups developing systems in a commercial enterprise, and research groups advancing the scienti?c state-of-the-art in academic and industrial settings. The objectives of this book are: (1) to serve as a motivation for de?ning future research programs in distributed computing, (2) to help identify areas where practitioners and engineers on the one hand and scientists and researchers on the other can improve the state of distributed computing through synergistic efforts, and (3) to motivate graduate students interested in entering the exciting research ?eld of distributed computing. |
| computer science berkeley requirements: DNA Computing Anne Condon, Grzegorz Rozenberg, 2003-06-29 The papers in this volume were presented at the 6th International Meeting on DNA Based Computers, organized by the Leiden Center for Natural Computing and held from June 13 to June 17, 2000 at The Lorentz Center, University of Leiden, Leiden, The Netherlands. DNA Computing is a novel and fascinating development at the interface of computer science and molecular biology. It has emerged in recent years, not simply as an exciting technology for information processing, but also as a catalyst for knowledge transfer between information processing, nanotechnology, and biology. This area of research has the potential to change our understanding of the theory and practice of computing. The call for papers and poster presentations sought contributions of original research and technical expositions in all areas of bio-computation. A total of 33 abstracts were submitted of which 16 were accepted for presentation and included in the proceedings. The papers were selected by the program committee based on originality and quality of research and on relevance to the bio-computing eld. Invited talks were given by Masami Hagiya (Tokyo University), Laura La- weber (Princeton University), John Reif (Duke University), Thomas Schmidt (Leiden University), and Lloyd M. Smith (University of Wisconsin). Invited - pers based on the talks by Hagiya and Reif are included in this volume, along with the contributed papers. Additional tutorials were held on the rst and last days of the conference. |
| computer science berkeley requirements: Handling Strings with R Gaston Sanchez, 2021-02-25 This book aims to help you get started with handling strings in R. It provides an overview of several resources that you can use for string manipulation. It covers useful functions in packages base and stringr, printing and formatting characters, regular expressions, and other tricks. |
| computer science berkeley requirements: Berkshire Encyclopedia of Human-computer Interaction William Sims Bainbridge, 2004 Presents a collection of articles on human-computer interaction, covering such topics as applications, methods, hardware, and computers and society. |
| computer science berkeley requirements: Advances in Computer and Information Sciences and Engineering Tarek Sobh, 2008-08-15 Advances in Computer and Information Sciences and Engineering includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Software Engineering, Computer Engineering, and Systems Engineering and Sciences. Advances in Computer and Information Sciences and Engineering includes selected papers from the conference proceedings of the International Conference on Systems, Computing Sciences and Software Engineering (SCSS 2007) which was part of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering (CISSE 2007). |
| computer science berkeley requirements: Quantum Computation American Mathematical Society. Short Course, Samuel J. Lomonaco, American Mathematical Society, 2002 This book presents written versions of the eight lectures given during the AMS Short Course held at the Joint Mathematics Meetings in Washington, D.C. The objective of this course was to share with the scientific community the many exciting mathematical challenges arising from the new field of quantum computation and quantum information science. The course was geared toward demonstrating the great breadth and depth of this mathematically rich research field. Interrelationships withexisting mathematical research areas were emphasized as much as possible. Moreover, the course was designed so that participants with little background in quantum mechanics would, upon completion, be prepared to begin reading the research literature on quantum computation and quantum informationscience. Based on audience feedback and questions, the written versions of the lectures have been greatly expanded, and supplementary material has been added. The book features an overview of relevant parts of quantum mechanics with an introduction to quantum computation, including many potential quantum mechanical computing devices; introduction to quantum algorithms and quantum complexity theory; in-depth discussion on quantum error correcting codes and quantum cryptography; and finally,exploration into diverse connections between quantum computation and various areas of mathematics and physics. |
| computer science berkeley requirements: Scientific and Technical Aerospace Reports , 1995 Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database. |
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A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation). Modern …
Computer | Definition, History, Operating Systems, & Facts
A computer is a programmable device for processing, storing, and displaying information. Learn more in this article about modern digital electronic …
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