| computer science requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: An Introduction to Berkeley UNIX Paul S. Wang, 1988 |
| computer science requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: Theoretical Statistics Robert W. Keener, 2010-09-08 Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential. The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis. The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix. |
| computer science requirements berkeley: 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 requirements berkeley: Learning Python Mark Lutz, 2013-06-12 Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code. Explore Python’s major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python’s general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python’s object-oriented programming tool for structuring code Write large programs with Python’s exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing |
| computer science requirements berkeley: Data Structures And Algorithms Shi-kuo Chang, 2003-09-29 This is an excellent, up-to-date and easy-to-use text on data structures and algorithms that is intended for undergraduates in computer science and information science. The thirteen chapters, written by an international group of experienced teachers, cover the fundamental concepts of algorithms and most of the important data structures as well as the concept of interface design. The book contains many examples and diagrams. Whenever appropriate, program codes are included to facilitate learning.This book is supported by an international group of authors who are experts on data structures and algorithms, through its website at www.cs.pitt.edu/~jung/GrowingBook/, so that both teachers and students can benefit from their expertise. |
| computer science requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: 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 requirements berkeley: Search User Interfaces Marti A. Hearst, 2009-09-21 The truly world-wide reach of the Web has brought with it a new realisation of the enormous importance of usability and user interface design. In the last ten years, much has become understood about what works in search interfaces from a usability perspective, and what does not. Researchers and practitioners have developed a wide range of innovative interface ideas, but only the most broadly acceptable make their way into major web search engines. This book summarizes these developments, presenting the state of the art of search interface design, both in academic research and in deployment in commercial systems. Many books describe the algorithms behind search engines and information retrieval systems, but the unique focus of this book is specifically on the user interface. It will be welcomed by industry professionals who design systems that use search interfaces as well as graduate students and academic researchers who investigate information systems. |
| computer science requirements berkeley: 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 requirements berkeley: How to Design Programs, second edition Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi, 2018-05-25 A completely revised edition, offering new design recipes for interactive programs and support for images as plain values, testing, event-driven programming, and even distributed programming. This introduction to programming places computer science at the core of a liberal arts education. Unlike other introductory books, it focuses on the program design process, presenting program design guidelines that show the reader how to analyze a problem statement, how to formulate concise goals, how to make up examples, how to develop an outline of the solution, how to finish the program, and how to test it. Because learning to design programs is about the study of principles and the acquisition of transferable skills, the text does not use an off-the-shelf industrial language but presents a tailor-made teaching language. For the same reason, it offers DrRacket, a programming environment for novices that supports playful, feedback-oriented learning. The environment grows with readers as they master the material in the book until it supports a full-fledged language for the whole spectrum of programming tasks. This second edition has been completely revised. While the book continues to teach a systematic approach to program design, the second edition introduces different design recipes for interactive programs with graphical interfaces and batch programs. It also enriches its design recipes for functions with numerous new hints. Finally, the teaching languages and their IDE now come with support for images as plain values, testing, event-driven programming, and even distributed programming. |
| computer science requirements berkeley: Principles of Cyber-Physical Systems Rajeev Alur, 2015-04-24 A foundational text that offers a rigorous introduction to the principles of design, specification, modeling, and analysis of cyber-physical systems. A cyber-physical system consists of a collection of computing devices communicating with one another and interacting with the physical world via sensors and actuators in a feedback loop. Increasingly, such systems are everywhere, from smart buildings to medical devices to automobiles. This textbook offers a rigorous and comprehensive introduction to the principles of design, specification, modeling, and analysis of cyber-physical systems. The book draws on a diverse set of subdisciplines, including model-based design, concurrency theory, distributed algorithms, formal methods of specification and verification, control theory, real-time systems, and hybrid systems, explaining the core ideas from each that are relevant to system design and analysis. The book explains how formal models provide mathematical abstractions to manage the complexity of a system design. It covers both synchronous and asynchronous models for concurrent computation, continuous-time models for dynamical systems, and hybrid systems for integrating discrete and continuous evolution. The role of correctness requirements in the design of reliable systems is illustrated with a range of specification formalisms and the associated techniques for formal verification. The topics include safety and liveness requirements, temporal logic, model checking, deductive verification, stability analysis of linear systems, and real-time scheduling algorithms. Principles of modeling, specification, and analysis are illustrated by constructing solutions to representative design problems from distributed algorithms, network protocols, control design, and robotics. This book provides the rapidly expanding field of cyber-physical systems with a long-needed foundational text by an established authority. It is suitable for classroom use or as a reference for professionals. |
| computer science requirements berkeley: The UNIX-haters Handbook Simson Garfinkel, Daniel Weise, Steven Strassmann, 1994 This book is for all people who are forced to use UNIX. It is a humorous book--pure entertainment--that maintains that UNIX is a computer virus with a user interface. It features letters from the thousands posted on the Internet's UNIX-Haters mailing list. It is not a computer handbook, tutorial, or reference. It is a self-help book that will let readers know they are not alone. |
| computer science requirements berkeley: The Mind Behind the Musical Ear Jeanne Shapiro Bamberger, 1991 Bamberger focuses on the earliest stages in the development of musical cognition. Beginning with children's invention of original rhythm notations, she follows eight-year-old Jeff as he reconstructs and invents descriptions of simple melodies. |
| computer science requirements berkeley: Computer Architecture John L. Hennessy, David A. Patterson, Krste Asanović, 2012 The computing world is in the middle of a revolution: mobile clients and cloud computing have emerged as the dominant paradigms driving programming and hardware innovation. This book focuses on the shift, exploring the ways in which software and technology in the 'cloud' are accessed by cell phones, tablets, laptops, and more |
| computer science requirements berkeley: Right College, Right Price Frank Palmasani, 2013 Describes how the Financial Fit program can help families determine how much college will really cost beyond the sticker price and factor cost into the college search, and explains how to maximize financial aid benefits. |
| computer science requirements berkeley: Mathematics by Experiment Jonathan Borwein, David Bailey, 2008-10-27 This revised and updated second edition maintains the content and spirit of the first edition and includes a new chapter, Recent Experiences, that provides examples of experimental mathematics that have come to light since the publication of the first edition in 2003. For more examples and insights, Experimentation in Mathematics: Computational P |
| computer science requirements berkeley: Computer Science Logo Style Brian Harvey, 1997 |
| computer science requirements berkeley: Gutsy Girls Of Science Ilina Singh, 2022-02-28 Eleven gutsy women who loved science enough to fight for their place in the sun... This book explores the contribution of these remarkable Indian women -- from cytogeneticist Archana Sharma and botanist Janaki Ammal to mathematician Raman Parimala, physicist Bibha Chowdhuri, chemist Asima Chatterjee and several others. This book is a celebration of their lives and the wonderful world of science. With intelligence and innate artistic talent, young Ilina Singh presents through this book 11 trailblazing Indian women who overcame all odds to achieve success in STEM. -- Eric Falt, Director and UNESCO Representative to Bhutan, India, Maldives and Sri Lanka The book includes a foreword by Eric Falt from UNESCO's Delhi office. |
| computer science requirements berkeley: Parallel Algorithms and Architectures Kurt Mehlhorn, 1987 |
| computer science requirements berkeley: Decision Procedures Daniel Kroening, Ofer Strichman, 2008-05-23 A decision procedure is an algorithm that, given a decision problem, terminates with a correct yes/no answer. Here, the authors focus on theories that are expressive enough to model real problems, but are still decidable. Specifically, the book concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research. The techniques described in the book draw from fields such as graph theory and logic, and are routinely used in industry. The authors introduce the basic terminology of satisfiability modulo theories and then, in separate chapters, study decision procedures for each of the following theories: propositional logic; equalities and uninterpreted functions; linear arithmetic; bit vectors; arrays; pointer logic; and quantified formulas. |
COMPUTER SCIENCE - discovery.berkeley.edu
Plan on 1 CS class & 1 math class/semester: Take CS10 and/or CS8 before CS61A, if no coding experience. See math requirements and AP/ IB policies and find calculus starting point. Check …
Electrical Engineering and Computer Sciences - University of …
Bachelor of Science (BS) The Berkeley Electrical Engineering and Computer Sciences major (EECS), offered through the College of Engineering, combines fundamentals of computer …
Undergraduate Programs At-A-Glance 2025
Mar 20, 2025 · Berkeley Engineering is among the top engineering programs in the nation as ranked by U.S. News & World Report. Undergraduate Engineering Ranked #3 (2025) 1st …
UC Berkeley Engineering undergraduate brochure
Berkeley Engineering is among the top engineering programs in the nation as ranked by U.S. News & World Report. ** Undergraduate programs in computer science are run through the …
ELECTRICAL ENGINEERING CONNECT WITH US AND …
The Electrical Engineering & Computer Sciences (EECS) major combines the fundamentals of computer science and electrical engineering in one major. The EECS major prepares …
Computer Science - University of California, Berkeley
For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page (https:// grad.berkeley.edu/admissions/steps-to …
TRANSFER QUANTITATIVE REASONING (QR) …
A student may satisfy this requirement by completing a course in College Math, Statistics or Computer Science equivalent to one ofered at UC Berkeley that fulfills Quantitative Reasoning …
UNIVERSITY OF CALIFORNIA, BERKELEY TRANSFER …
The requirements for admission as a transfer student vary by college. Requirements for all majors are available on assist.org or in the Berkeley Academic Guide (guide.berkeley.edu).
DISC OVER BERK ELEY - Office of Undergraduate Admissions
To be considered for admission to UC Berkeley, international applicants must finish secondary school and earn a certificate of completion, which allows admission to a university in their …
Berkeley Engineering Undergraduate At-A-Glance Brochure
Some requirements can be satisfied with Advanced Placement (AP), International Baccalaureate (IB), A-Level, and transfer credit. AP, IB or A-Level exams can satisfy no more than two of the …
Transfer Requirements - El Camino College
or L&S requirements. ECC's Computer Science 2 and 30 articulates as Berkeley's Computer Science 61B (students must complete COMPSCI 47B at UCB to complete the requirement). …
University of California Berkeley College of Letters & Science …
university requirements (see https://lsadvising.berkeley.edu/ for details). A minimum of 13 units must be listed for each semester unless you are approved for a reduced course list by Letters …
Computer Science Major Requirements Berkeley (2024)
Computer Science Major Requirements Berkeley: Structure and Interpretation of Computer Programs Harold Abelson,Gerald Jay Sussman,2022-05-03 A new version of the classic and …
TRANSFER QUANTITATIVE REASONING (QR) …
A student may satisfy this requirement by completing a course in College Math, Statistics or Computer Science equivalent to one ofered at UC Berkeley that fulfills Quantitative Reasoning …
Cognitive Science 1 Cognitive Science Students admitted to …
For prerequisites required before declaring the major, please see the Major Requirements tab. Once prerequisites are completed, students may submit the Cognitive Science declaration …
Computer Science Major Requirements Berkeley [PDF]
Computer Science Major Requirements Berkeley: Structure and Interpretation of Computer Programs Harold Abelson,Gerald Jay Sussman,2022-05-03 A new version of the classic and …
Quantitative Reasoning Requirement - University of …
The following Berkeley course options, completed with a letter grade of C- or higher, satisfy the Quantitative Reasoning requirement: COMPSCI C8 Foundations of Data Science 4
Computer Science Major Requirements Berkeley
Computer Science Major Requirements Berkeley: Structure and Interpretation of Computer Programs Harold Abelson,Gerald Jay Sussman,2022-05-03 A new version of the classic and …
Computer Science - University of California, Berkeley
Computer Science majors with an overall GPA of 3.70 or above are eligible to apply to the EECS honors degree program. A minor in Computer Science is available to all undergraduate …
COMPUTER SCIENCE - discovery.berkeley.edu
Plan on 1 CS class & 1 math class/semester: Take CS10 and/or CS8 before CS61A, if no coding experience. See math requirements and AP/ IB policies and find calculus starting point. Check …
Electrical Engineering and Computer Sciences - University of …
Bachelor of Science (BS) The Berkeley Electrical Engineering and Computer Sciences major (EECS), offered through the College of Engineering, combines fundamentals of computer …
Undergraduate Programs At-A-Glance 2025
Mar 20, 2025 · Berkeley Engineering is among the top engineering programs in the nation as ranked by U.S. News & World Report. Undergraduate Engineering Ranked #3 (2025) 1st …
College of Computing, Data Science, and Society
The undergraduate major programs in computer science, data science, and statistics have transitioned from the College of Letters & Science to CDSS. Students who were admitted in …
UC Berkeley Engineering undergraduate brochure
Berkeley Engineering is among the top engineering programs in the nation as ranked by U.S. News & World Report. ** Undergraduate programs in computer science are run through the …
ELECTRICAL ENGINEERING CONNECT WITH US AND …
The Electrical Engineering & Computer Sciences (EECS) major combines the fundamentals of computer science and electrical engineering in one major. The EECS major prepares …
Computer Science - University of California, Berkeley
For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page (https:// grad.berkeley.edu/admissions/steps-to …
TRANSFER QUANTITATIVE REASONING (QR) REQUIREMENT
A student may satisfy this requirement by completing a course in College Math, Statistics or Computer Science equivalent to one ofered at UC Berkeley that fulfills Quantitative Reasoning …
UNIVERSITY OF CALIFORNIA, BERKELEY TRANSFER …
The requirements for admission as a transfer student vary by college. Requirements for all majors are available on assist.org or in the Berkeley Academic Guide (guide.berkeley.edu).
DISC OVER BERK ELEY - Office of Undergraduate Admissions
To be considered for admission to UC Berkeley, international applicants must finish secondary school and earn a certificate of completion, which allows admission to a university in their …
Berkeley Engineering Undergraduate At-A-Glance Brochure
Some requirements can be satisfied with Advanced Placement (AP), International Baccalaureate (IB), A-Level, and transfer credit. AP, IB or A-Level exams can satisfy no more than two of the …
Transfer Requirements - El Camino College
or L&S requirements. ECC's Computer Science 2 and 30 articulates as Berkeley's Computer Science 61B (students must complete COMPSCI 47B at UCB to complete the requirement). …
University of California Berkeley College of Letters & Science …
university requirements (see https://lsadvising.berkeley.edu/ for details). A minimum of 13 units must be listed for each semester unless you are approved for a reduced course list by Letters …
Computer Science Major Requirements Berkeley (2024)
Computer Science Major Requirements Berkeley: Structure and Interpretation of Computer Programs Harold Abelson,Gerald Jay Sussman,2022-05-03 A new version of the classic and …
TRANSFER QUANTITATIVE REASONING (QR) REQUIREMENT
A student may satisfy this requirement by completing a course in College Math, Statistics or Computer Science equivalent to one ofered at UC Berkeley that fulfills Quantitative Reasoning …
Cognitive Science 1 Cognitive Science Students admitted to …
For prerequisites required before declaring the major, please see the Major Requirements tab. Once prerequisites are completed, students may submit the Cognitive Science declaration …
Computer Science Major Requirements Berkeley [PDF]
Computer Science Major Requirements Berkeley: Structure and Interpretation of Computer Programs Harold Abelson,Gerald Jay Sussman,2022-05-03 A new version of the classic and …
Quantitative Reasoning Requirement - University of California, …
The following Berkeley course options, completed with a letter grade of C- or higher, satisfy the Quantitative Reasoning requirement: COMPSCI C8 Foundations of Data Science 4
Computer Science Major Requirements Berkeley
Computer Science Major Requirements Berkeley: Structure and Interpretation of Computer Programs Harold Abelson,Gerald Jay Sussman,2022-05-03 A new version of the classic and …
