| computer science 101 stanford online: Introduction to Information Retrieval Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, 2008-07-07 Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures. |
| computer science 101 stanford online: Programming Methodology Annabelle Mclver, Carroll Morgan, 2002-10-01 Written by the members of the IFIP Working Group 2.3 (Programming Methodology) this text constitutes an exciting reference on the front-line of research activity in programming methodology. The range of subjects reflects the current interests of the members, and will offer insightful and controversial opinions on modern programming methods and practice. The material is arranged in thematic sections, each one introduced by a problem which epitomizes the spirit of that topic. The exemplary problem will encourage vigorous discussion and will form the basis for an introduction/tutorial for its section. |
| computer science 101 stanford online: Applied Cryptography Bruce Schneier, 2017-05-25 From the world's most renowned security technologist, Bruce Schneier, this 20th Anniversary Edition is the most definitive reference on cryptography ever published and is the seminal work on cryptography. Cryptographic techniques have applications far beyond the obvious uses of encoding and decoding information. For developers who need to know about capabilities, such as digital signatures, that depend on cryptographic techniques, there's no better overview than Applied Cryptography, the definitive book on the subject. Bruce Schneier covers general classes of cryptographic protocols and then specific techniques, detailing the inner workings of real-world cryptographic algorithms including the Data Encryption Standard and RSA public-key cryptosystems. The book includes source-code listings and extensive advice on the practical aspects of cryptography implementation, such as the importance of generating truly random numbers and of keeping keys secure. . . .the best introduction to cryptography I've ever seen. . . .The book the National Security Agency wanted never to be published. . . . -Wired Magazine . . .monumental . . . fascinating . . . comprehensive . . . the definitive work on cryptography for computer programmers . . . -Dr. Dobb's Journal . . .easily ranks as one of the most authoritative in its field. -PC Magazine The book details how programmers and electronic communications professionals can use cryptography-the technique of enciphering and deciphering messages-to maintain the privacy of computer data. It describes dozens of cryptography algorithms, gives practical advice on how to implement them into cryptographic software, and shows how they can be used to solve security problems. The book shows programmers who design computer applications, networks, and storage systems how they can build security into their software and systems. With a new Introduction by the author, this premium edition will be a keepsake for all those committed to computer and cyber security. |
| computer science 101 stanford online: Program Arcade Games Paul Craven, 2015-12-31 Learn and use Python and PyGame to design and build cool arcade games. In Program Arcade Games: With Python and PyGame, Second Edition, Dr. Paul Vincent Craven teaches you how to create fun and simple quiz games; integrate and start using graphics; animate graphics; integrate and use game controllers; add sound and bit-mapped graphics; and build grid-based games. After reading and using this book, you'll be able to learn to program and build simple arcade game applications using one of today's most popular programming languages, Python. You can even deploy onto Steam and other Linux-based game systems as well as Android, one of today's most popular mobile and tablet platforms. You'll learn: How to create quiz games How to integrate and start using graphics How to animate graphics How to integrate and use game controllers How to add sound and bit-mapped graphics How to build grid-based games Audience“div>This book assumes no prior programming knowledge. |
| computer science 101 stanford online: Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on the Growth of Computer Science Undergraduate Enrollments, 2018-04-28 The field of computer science (CS) is currently experiencing a surge in undergraduate degree production and course enrollments, which is straining program resources at many institutions and causing concern among faculty and administrators about how best to respond to the rapidly growing demand. There is also significant interest about what this growth will mean for the future of CS programs, the role of computer science in academic institutions, the field as a whole, and U.S. society more broadly. Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments seeks to provide a better understanding of the current trends in computing enrollments in the context of past trends. It examines drivers of the current enrollment surge, relationships between the surge and current and potential gains in diversity in the field, and the potential impacts of responses to the increased demand for computing in higher education, and it considers the likely effects of those responses on students, faculty, and institutions. This report provides recommendations for what institutions of higher education, government agencies, and the private sector can do to respond to the surge and plan for a strong and sustainable future for the field of CS in general, the health of the institutions of higher education, and the prosperity of the nation. |
| computer science 101 stanford online: Computer Science Handbook Allen B. Tucker, 2004-06-28 When you think about how far and fast computer science has progressed in recent years, it's not hard to conclude that a seven-year old handbook may fall a little short of the kind of reference today's computer scientists, software engineers, and IT professionals need. With a broadened scope, more emphasis on applied computing, and more than 70 chap |
| computer science 101 stanford online: Principles of Computer System Design Jerome H. Saltzer, M. Frans Kaashoek, 2009-05-21 Principles of Computer System Design is the first textbook to take a principles-based approach to the computer system design. It identifies, examines, and illustrates fundamental concepts in computer system design that are common across operating systems, networks, database systems, distributed systems, programming languages, software engineering, security, fault tolerance, and architecture.Through carefully analyzed case studies from each of these disciplines, it demonstrates how to apply these concepts to tackle practical system design problems. To support the focus on design, the text identifies and explains abstractions that have proven successful in practice such as remote procedure call, client/service organization, file systems, data integrity, consistency, and authenticated messages. Most computer systems are built using a handful of such abstractions. The text describes how these abstractions are implemented, demonstrates how they are used in different systems, and prepares the reader to apply them in future designs.The book is recommended for junior and senior undergraduate students in Operating Systems, Distributed Systems, Distributed Operating Systems and/or Computer Systems Design courses; and professional computer systems designers. - Concepts of computer system design guided by fundamental principles - Cross-cutting approach that identifies abstractions common to networking, operating systems, transaction systems, distributed systems, architecture, and software engineering - Case studies that make the abstractions real: naming (DNS and the URL); file systems (the UNIX file system); clients and services (NFS); virtualization (virtual machines); scheduling (disk arms); security (TLS) - Numerous pseudocode fragments that provide concrete examples of abstract concepts - Extensive support. The authors and MIT OpenCourseWare provide on-line, free of charge, open educational resources, including additional chapters, course syllabi, board layouts and slides, lecture videos, and an archive of lecture schedules, class assignments, and design projects |
| computer science 101 stanford online: Introduction to Applied Linear Algebra Stephen Boyd, Lieven Vandenberghe, 2018-06-07 A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples. |
| computer science 101 stanford online: Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers Stephen Boyd, Neal Parikh, Eric Chu, 2011 Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. |
| computer science 101 stanford online: Creating a Website: The Missing Manual Matthew MacDonald, 2015-06-18 You can easily create a professional-looking website with nothing more than an ordinary computer and some raw ambition. Want to build a blog, sell products, create forums, or promote an event? No problem! This friendly, jargon-free book gives you the techniques, tools, and advice you need to build a site and get it up on the Web. The important stuff you need to know: Master the basics. Learn HTML5, the language of the Web. Design good-looking pages. Use styles to build polished layouts. Get it online. Find a reliable web host and pick a good web address. Use time-saving tools. Learn free tools for creating web pages and tracking your visitors. Attract visitors. Make sure people can find your site through popular search engines like Google. Build a community. Encourage repeat visits with social media. Bring in the cash. Host Google ads, sell Amazon’s wares, or push your own products that people can buy via PayPal. Add pizzazz. Include audio, video, interactive menus, and a pinch of JavaScript. |
| computer science 101 stanford online: Science and Cooking: Physics Meets Food, From Homemade to Haute Cuisine Michael Brenner, Pia Sörensen, David Weitz, 2020-10-20 Based on the popular Harvard University and edX course, Science and Cooking explores the scientific basis of why recipes work. The spectacular culinary creations of modern cuisine are the stuff of countless articles and social media feeds. But to a scientist they are also perfect pedagogical explorations into the basic scientific principles of cooking. In Science and Cooking, Harvard professors Michael Brenner, Pia Sörensen, and David Weitz bring the classroom to your kitchen to teach the physics and chemistry underlying every recipe. Why do we knead bread? What determines the temperature at which we cook a steak, or the amount of time our chocolate chip cookies spend in the oven? Science and Cooking answers these questions and more through hands-on experiments and recipes from renowned chefs such as Christina Tosi, Joanne Chang, and Wylie Dufresne, all beautifully illustrated in full color. With engaging introductions from revolutionary chefs and collaborators Ferran Adria and José Andrés, Science and Cooking will change the way you approach both subjects—in your kitchen and beyond. |
| computer science 101 stanford online: Foundations of Security Christoph Kern, Anita Kesavan, Neil Daswani, 2007-05-11 Software developers need to worry about security as never before. They need clear guidance on safe coding practices, and that’s exactly what this book delivers. The book does not delve deep into theory, or rant about the politics of security. Instead, it clearly and simply lays out the most common threats that programmers need to defend against. It then shows programmers how to make their defense. The book takes a broad focus, ranging over SQL injection, worms and buffer overflows, password security, and more. It sets programmers on the path towards successfully defending against the entire gamut of security threats that they might face. |
| computer science 101 stanford online: Computer Organization and Design RISC-V Edition David A. Patterson, John L. Hennessy, 2017-05-12 The new RISC-V Edition of Computer Organization and Design features the RISC-V open source instruction set architecture, the first open source architecture designed to be used in modern computing environments such as cloud computing, mobile devices, and other embedded systems. With the post-PC era now upon us, Computer Organization and Design moves forward to explore this generational change with examples, exercises, and material highlighting the emergence of mobile computing and the Cloud. Updated content featuring tablet computers, Cloud infrastructure, and the x86 (cloud computing) and ARM (mobile computing devices) architectures is included. An online companion Web site provides advanced content for further study, appendices, glossary, references, and recommended reading. - Features RISC-V, the first such architecture designed to be used in modern computing environments, such as cloud computing, mobile devices, and other embedded systems - Includes relevant examples, exercises, and material highlighting the emergence of mobile computing and the cloud |
| computer science 101 stanford online: HTML5 Foundations Matt West, 2012-11-19 Master innovative and eye-catching website design with the exciting new Treehouse Series of books Turn plain words and images into stunning websites using HTML5 and this beautiful, full-colour guide. Taking you beyond the constraints of prebuilt themes and simple site building tools, this new Treehouse book combines practicality with inspiration to show you how to create fully customized, modern, and dazzling websites that make viewers want to stop and stay. The exciting new Treehouse Series of books is authored by Treehouse experts and packed with innovative design ideas and practical skill-building. If you're a web developer, web designer, hobbyist, or career-changer, every book in this practical new series should be on your bookshelf. Part of the new Treehouse Series of books, teaching you effective and compelling website development and design, helping you build practical skills Provides career-worthy information from Treehouse industry pros and trainers Explains HTML5 basics, such as how to format text, add scripts to pages, and use HTML5 for audio and video Also covers hypermedia, CSS and JavaScript, embedding video, geolocation, and much more Leverage pages of dazzling website design ideas and expert instruction with a new Treehouse Series book. |
| computer science 101 stanford online: Learn to Code HTML and CSS Shay Howe, 2014-04-22 HTML and CSS can be a little daunting at first but fear not. This book, based on Shay Howe's popular workshop covers the basics and breaks down the barrier to entry, showing readers how they can start using HTML and CSS through practical techniques today. They'll find accompanying code examples online, while they explore topics such as the different structures of HTML and CSS, and common terms. After establishing a basic understanding of HTML and CSS a deeper dive is taken into the box model and how to work with floats. The book includes an exercise focused on cleaning up a web page by improving the user interface and design, solely using HTML and CSS. With a few quick changes the web page changes shape and comes to life. Interactive, technically up-to-the-minute and easy-to-understand, this book will advance a student's skills to a professional level. |
| computer science 101 stanford online: Introduction to Mathematical Thinking Keith J. Devlin, 2012 Mathematical thinking is not the same as 'doing math'--unless you are a professional mathematician. For most people, 'doing math' means the application of procedures and symbolic manipulations. Mathematical thinking, in contrast, is what the name reflects, a way of thinking about things in the world that humans have developed over three thousand years. It does not have to be about mathematics at all, which means that many people can benefit from learning this powerful way of thinking, not just mathematicians and scientists.--Back cover. |
| computer science 101 stanford online: Speech & Language Processing Dan Jurafsky, 2000-09 |
| computer science 101 stanford online: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| computer science 101 stanford online: Twenty Lectures on Algorithmic Game Theory Tim Roughgarden, 2016-08-30 Computer science and economics have engaged in a lively interaction over the past fifteen years, resulting in the new field of algorithmic game theory. Many problems that are central to modern computer science, ranging from resource allocation in large networks to online advertising, involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, and concepts from computer science are increasingly important in economics. This book grew out of the author's Stanford University course on algorithmic game theory, and aims to give students and other newcomers a quick and accessible introduction to many of the most important concepts in the field. The book also includes case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management. |
| computer science 101 stanford online: Is-703.a Fema, 2010-01-15 Course Overview This course introduces resource management as described in the National Incident Management System (NIMS), and shows how systems for managing resources can be used to improve incident response. The course includes examples of best practices, lessons learned, and job aids to assist the participant in planning for resource management. The course includes the following lessons: -Lesson 1: Resource Management Overview -Lesson 2: Resource Management Planning -Lesson 3: Resource Typing & Readiness -Lesson 4: Resource Management During Incidents -Lesson 5: Resource Management & Complex Incidents -Lesson 6: Course Summary Note: IS-703.a is an updated version of the IS-703 course. The update version of the course includes expanded content about resource typing. If you have successfully completed IS-703, you may want to review the new version of the course, especially lesson 3. For credentialing purposes, the courses are equivalent. Course Objectives: At the conclusion of this course, you should be able to: -Establish systems for describing, inventorying, requesting, and tracking resources. -Activate these systems prior to and during an incident. -Dispatch resources prior to and during an incident. -Deactivate or recall resources during or after incidents. Primary Audience Federal, State, tribal, and local emergency managers; first responders to include incident commanders from all emergency management disciplines; private industry personnel responsible for coordination activities during a disaster; and voluntary agency personnel. Prerequisites N/A. However, completion of IS 700.a, National Incident Management System (NIMS), An Introduction, is recommended. |
| computer science 101 stanford online: 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 101 stanford online: Python for Everybody Charles R. Severance, 2016-04-09 Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled Python for Informatics: Exploring Information.There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course. |
| computer science 101 stanford online: Reinforcement Learning, second edition Richard S. Sutton, Andrew G. Barto, 2018-11-13 The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning. |
| computer science 101 stanford online: Computer Science Robert Sedgewick, Kevin Wayne, 2016-06-17 Named a Notable Book in the 21st Annual Best of Computing list by the ACM! Robert Sedgewick and Kevin Wayne’s Computer Science: An Interdisciplinary Approach is the ideal modern introduction to computer science with Java programming for both students and professionals. Taking a broad, applications-based approach, Sedgewick and Wayne teach through important examples from science, mathematics, engineering, finance, and commercial computing. The book demystifies computation, explains its intellectual underpinnings, and covers the essential elements of programming and computational problem solving in today’s environments. The authors begin by introducing basic programming elements such as variables, conditionals, loops, arrays, and I/O. Next, they turn to functions, introducing key modular programming concepts, including components and reuse. They present a modern introduction to object-oriented programming, covering current programming paradigms and approaches to data abstraction. Building on this foundation, Sedgewick and Wayne widen their focus to the broader discipline of computer science. They introduce classical sorting and searching algorithms, fundamental data structures and their application, and scientific techniques for assessing an implementation’s performance. Using abstract models, readers learn to answer basic questions about computation, gaining insight for practical application. Finally, the authors show how machine architecture links the theory of computing to real computers, and to the field’s history and evolution. For each concept, the authors present all the information readers need to build confidence, together with examples that solve intriguing problems. Each chapter contains question-and-answer sections, self-study drills, and challenging problems that demand creative solutions. Companion web site (introcs.cs.princeton.edu/java) contains Extensive supplementary information, including suggested approaches to programming assignments, checklists, and FAQs Graphics and sound libraries Links to program code and test data Solutions to selected exercises Chapter summaries Detailed instructions for installing a Java programming environment Detailed problem sets and projects Companion 20-part series of video lectures is available at informit.com/title/9780134493831 |
| computer science 101 stanford online: Computing the Future National Research Council, Computer Science and Telecommunications Board, Committee to Assess the Scope and Direction of Computer Science and Technology, 1992-02-01 Computers are increasingly the enabling devices of the information revolution, and computing is becoming ubiquitous in every corner of society, from manufacturing to telecommunications to pharmaceuticals to entertainment. Even more importantly, the face of computing is changing rapidly, as even traditional rivals such as IBM and Apple Computer begin to cooperate and new modes of computing are developed. Computing the Future presents a timely assessment of academic computer science and engineering (CS&E), examining what should be done to ensure continuing progress in making discoveries that will carry computing into the twenty-first century. Most importantly, it advocates a broader research and educational agenda that builds on the field's impressive accomplishments. The volume outlines a framework of priorities for CS&E, along with detailed recommendations for education, funding, and leadership. A core research agenda is outlined for these areas: processors and multiple-processor systems, data communications and networking, software engineering, information storage and retrieval, reliability, and user interfaces. This highly readable volume examines: Computer science and engineering as a discipline-how computer scientists and engineers are pushing back the frontiers of their field. How CS&E must change to meet the challenges of the future. The influence of strategic investment by federal agencies in CS&E research. Recent structural changes that affect the interaction of academic CS&E and the business environment. Specific examples of interdisciplinary and applications research in four areas: earth sciences and the environment, computational biology, commercial computing, and the long-term goal of a national electronic library. The volume provides a detailed look at undergraduate CS&E education, highlighting the limitations of four-year programs, and discusses the emerging importance of a master's degree in CS&E and the prospects for broadening the scope of the Ph.D. It also includes a brief look at continuing education. |
| computer science 101 stanford online: The Cambridge Handbook of Computing Education Research Sally A. Fincher, Anthony V. Robins, 2019-02-13 This is an authoritative introduction to Computing Education research written by over 50 leading researchers from academia and the industry. |
| computer science 101 stanford online: Bitcoin and Cryptocurrency Technologies Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder, 2016-07-19 An authoritative introduction to the exciting new technologies of digital money Bitcoin and Cryptocurrency Technologies provides a comprehensive introduction to the revolutionary yet often misunderstood new technologies of digital currency. Whether you are a student, software developer, tech entrepreneur, or researcher in computer science, this authoritative and self-contained book tells you everything you need to know about the new global money for the Internet age. How do Bitcoin and its block chain actually work? How secure are your bitcoins? How anonymous are their users? Can cryptocurrencies be regulated? These are some of the many questions this book answers. It begins by tracing the history and development of Bitcoin and cryptocurrencies, and then gives the conceptual and practical foundations you need to engineer secure software that interacts with the Bitcoin network as well as to integrate ideas from Bitcoin into your own projects. Topics include decentralization, mining, the politics of Bitcoin, altcoins and the cryptocurrency ecosystem, the future of Bitcoin, and more. An essential introduction to the new technologies of digital currency Covers the history and mechanics of Bitcoin and the block chain, security, decentralization, anonymity, politics and regulation, altcoins, and much more Features an accompanying website that includes instructional videos for each chapter, homework problems, programming assignments, and lecture slides Also suitable for use with the authors' Coursera online course Electronic solutions manual (available only to professors) |
| computer science 101 stanford online: Negotiating for Success: Essential Strategies and Skills George J. Siedel, 2014-10-04 We all negotiate on a daily basis. We negotiate with our spouses, children, parents, and friends. We negotiate when we rent an apartment, buy a car, purchase a house, and apply for a job. Your ability to negotiate might even be the most important factor in your career advancement. Negotiation is also the key to business success. No organization can survive without contracts that produce profits. At a strategic level, businesses are concerned with value creation and achieving competitive advantage. But the success of high-level business strategies depends on contracts made with suppliers, customers, and other stakeholders. Contracting capability—the ability to negotiate and perform successful contracts—is the most important function in any organization. This book is designed to help you achieve success in your personal negotiations and in your business transactions. The book is unique in two ways. First, the book not only covers negotiation concepts, but also provides practical actions you can take in future negotiations. This includes a Negotiation Planning Checklist and a completed example of the checklist for your use in future negotiations. The book also includes (1) a tool you can use to assess your negotiation style; (2) examples of “decision trees,” which are useful in calculating your alternatives if your negotiation is unsuccessful; (3) a three-part strategy for increasing your power during negotiations; (4) a practical plan for analyzing your negotiations based on your reservation price, stretch goal, most-likely target, and zone of potential agreement; (5) clear guidelines on ethical standards that apply to negotiations; (6) factors to consider when deciding whether you should negotiate through an agent; (7) psychological tools you can use in negotiations—and traps to avoid when the other side uses them; (8) key elements of contract law that arise during negotiations; and (9) a checklist of factors to use when you evaluate your performance as a negotiator. Second, the book is unique in its holistic approach to the negotiation process. Other books often focus narrowly either on negotiation or on contract law. Furthermore, the books on negotiation tend to focus on what happens at the bargaining table without addressing the performance of an agreement. These books make the mistaken assumption that success is determined by evaluating the negotiation rather than evaluating performance of the agreement. Similarly, the books on contract law tend to focus on the legal requirements for a contract to be valid, thus giving short shrift to the negotiation process that precedes the contract and to the performance that follows. In the real world, the contracting process is not divided into independent phases. What happens during a negotiation has a profound impact on the contract and on the performance that follows. The contract’s legal content should reflect the realities of what happened at the bargaining table and the performance that is to follow. This book, in contrast to others, covers the entire negotiation process in chronological order beginning with your decision to negotiate and continuing through the evaluation of your performance as a negotiator. A business executive in one of the negotiation seminars the author teaches as a University of Michigan professor summarized negotiation as follows: “Life is negotiation!” No one ever stated it better. As a mother with young children and as a company leader, the executive realized that negotiations are pervasive in our personal and business lives. With its emphasis on practical action, and with its chronological, holistic approach, this book provides a roadmap you can use when navigating through your life as a negotiator. |
| computer science 101 stanford online: Mathematical Writing Donald E. Knuth, Tracy Larrabee, Paul M. Roberts, 1989 This book will help those wishing to teach a course in technical writing, or who wish to write themselves. |
| computer science 101 stanford online: Algorithms Robert Sedgewick, Kevin Wayne, 2014-02-01 This book is Part I of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part I contains Chapters 1 through 3 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the Online Course link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience. |
| computer science 101 stanford online: Natural Language Processing with Python Steven Bird, Ewan Klein, Edward Loper, 2009-06-12 This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
| computer science 101 stanford online: Convex Optimization Stephen P. Boyd, Lieven Vandenberghe, 2004-03-08 Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics. |
| computer science 101 stanford online: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| computer science 101 stanford online: 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 101 stanford online: Art and Science of Java Eric Roberts, 2013-07-17 In The Art and Science of Java, Stanford professor and well-known leader in Computer Science Education Eric Roberts emphasizes the reader-friendly exposition that led to the success of The Art and Science of C. By following the recommendations of the Association of Computing Machinery's Java Task Force, this first edition text adopts a modern objects-first approach that introduces readers to useful hierarchies from the very beginning. Introduction; Programming by Example; Expressions; Statement Forms; Methods; Objects and Classes; Objects and Memory; Strings and Characters; Object-Oriented Graphics; Event-Driven Programs; Arrays and ArrayLists; Searching and Sorting; Collection Classes; Looking Ahead. A modern objects-first approach to the Java programming language that introduces readers to useful class hierarchies from the very beginning. |
| computer science 101 stanford online: The Algorithm Design Manual Steven S Skiena, 2009-04-05 This newly expanded and updated second edition of the best-selling classic continues to take the mystery out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW war stories relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java |
| computer science 101 stanford online: Deep Learning in Natural Language Processing Li Deng, Yang Liu, 2018-05-23 In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing. |
| computer science 101 stanford online: Introduction to Programming Using Java David Eck, 2009-09 This is a free, on-line textbook on introductory programming using Java. This book is directed mainly towards beginning programmers, although it might also be useful for experienced programmers who want to learn more about Java. It is an introductory text and does not provide complete coverage of the Java language. The text is a PDF and is suitable for printing or on-screen reading. It contains internal links for navigation and external links to source code files, exercise solutions, and other resources. Contents: 1) Overview: The Mental Landscape. 2) Programming in the Small I: Names and Things. 3) Programming in the Small II: Control. 4) Programming in the Large I: Subroutines. 5) Programming in the Large II: Objects and Classes. 6) Introduction to GUI Programming. 7) Arrays. 8) Correctness and Robustness. 9) Linked Data Structures and Recursion. 10) Generic Programming and Collection Classes. 11) Files and Networking. 12) Advanced GUI Programming. Appendices: Source Code for All Examples in this Book, and News and Errata. |
| computer science 101 stanford online: The Last Lecture Randy Pausch, Jeffrey Zaslow, 2010 The author, a computer science professor diagnosed with terminal cancer, explores his life, the lessons that he has learned, how he has worked to achieve his childhood dreams, and the effect of his diagnosis on him and his family. |
| computer science 101 stanford online: Work & Social Justice David Bacon, Roberto G. Trujillo, Benjamin Lee Stone, José R. Padilla (California lawyer), Michael A. Keller, Ignacio Ornelas Rodriguez, 2020 Published by the Stanford Libraries in conjunction with the exhibition Work & Social Justice: The David Bacon Photography Archive at Stanford in the Peterson Gallery & Munger Rotunda of Cecil H. Green Library, Stanford University, October 8, 2020 through January 24, 2020. |
CS110 Lecture 1: Introduction - Stanford University
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Lecture 01: Welcome to CS110: P rinciples of Computer Systems
Not stocked at Stanford Bookstore b y design. You can buy a copy of it from Amazon if y ou want. "Computer Systems: A Programmer's Perspective", by Bryant and O'Hallaron "Principles of …
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What is a computer network? Why study computer networks? Nodes: Computers, dedicated routers, . . . Links: Coax, twisted pair, fibers, radio . . . ... But how do you build something like …
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computer science 101 please note: some online courses may draw on material from courses taught on-campus but they are not equivalent to on-campus courses. this statement does not …
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Mar 21, 2017 · Stanford University Lecturer, Computer Science Nick Parlante March 20, 2017 Carlo Carandang has successfully completed a free online offering of Computer Science 101 …
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Computer Science 101 By Stanford University Allen B. Tucker Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments National Academies of
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Knowing computer: Basic Applications of Computer; Components of Computer System, Central Processing Unit (CPU), VDU, Keyboard and Mouse, Other input/output Devices, Computer …
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Stanford University – Computer Science 101 A free online course provided by Stanford University, it assumes no prior knowledge and seeks to demystify the key ideas and principles of …
Essential C - Stanford University
This Stanford CS Education document tries to summarize all the basic features of the C language. The coverage is pretty quick, so it is most appropriate as review or for someone with some …
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• Most computer nets connected by Internet protocol - Runs over a variety of physical networks, so can connect Ethernet, Wireless, people behind modem lines, etc.
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Computer science is the art of solving problems with computers. This is a broad definition that encom-passes an equally broad field. Within computer science, we find software engineering, …
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Lecture 01: Welcome to CS110: P rinciples of Computer …
Not stocked at Stanford Bookstore b y design. You can buy a copy of it from Amazon if y ou want. "Computer Systems: A Programmer's Perspective", by Bryant and O'Hallaron "Principles of …
CS144 – Introduction to Computer Networking - Stanford …
What is a computer network? Why study computer networks? Nodes: Computers, dedicated routers, . . . Links: Coax, twisted pair, fibers, radio . . . ... But how do you build something like …
Stanford Damjan Ostrelič STATEMENT OF …
computer science 101 please note: some online courses may draw on material from courses taught on-campus but they are not equivalent to on-campus courses. this statement does not …
March 20, 2017 Carlo Carandang
Mar 21, 2017 · Stanford University Lecturer, Computer Science Nick Parlante March 20, 2017 Carlo Carandang has successfully completed a free online offering of Computer Science 101 …
Computer Science 101 By Stanford University (book)
Computer Science 101 By Stanford University Allen B. Tucker Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments National Academies of
Computer Science 101 - prod-cert-bucket.s3.amazonaws.com
please note: some online courses may draw on material from courses taught on-campus but they are not equivalent to on-campus courses. THIS STATEMENT DOES NOT AFFIRM THAT …
Computer Science 101 By Stanford University Copy
Computer Science Undergraduate Enrollments seeks to provide a better understanding of the current trends in computing enrollments in the context of past trends It examines drivers of the …
CS 106A, Lecture 1 - Stanford University
What is Computer Science? •The art of using computing to solve complex problems. –Specify instructionsthat computers execute, usually in a programming language •Applicable to art, …
Suggested Reading 1. Introduction to Computers, Peter …
Knowing computer: Basic Applications of Computer; Components of Computer System, Central Processing Unit (CPU), VDU, Keyboard and Mouse, Other input/output Devices, Computer …
Computer Science Culture Vulture Recommendations - trc.ac.uk
Stanford University – Computer Science 101 A free online course provided by Stanford University, it assumes no prior knowledge and seeks to demystify the key ideas and principles of …
Essential C - Stanford University
This Stanford CS Education document tries to summarize all the basic features of the C language. The coverage is pretty quick, so it is most appropriate as review or for someone with some …
CS144 – Introduction to Computer Networking - Stanford …
• Most computer nets connected by Internet protocol - Runs over a variety of physical networks, so can connect Ethernet, Wireless, people behind modem lines, etc.
Cursos Gratuitos en Línea – Universidad de Stanford y …
Cursos Gratuitos en Línea – Universidad de Stanford y Desafío Latam Aprovecha la oportunidad de formarte con cursos gratuitos en áreas clave como ciencia de la computación, inteligencia …
Computer Science and the Stanford Honor Code
Stanford employs powerful automated plagiarism detection tools that compare assignment submissions with other submissions from the current and previous quarters. The tools also …
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computer science 101 please note: some online courses may draw on material from courses taught on-campus but they are not equivalent to on-campus courses. this statement does not …
2. Using Information Technology, Brian K williams, Stacey C …
Introduction to Internet, WWW and Web Browsers: Basic of Computer networks; LAN, WAN; Concept of Internet; Applications of Internet: connecting to internet; ISP; Knowing the Internet; …
Stanford Sergey Vilgelm STATEMENT OF ACCOMPLISHMENT …
computer science 101 please note: some online courses may draw on material from courses taught on-campus but they are not equivalent to on-campus courses. this statement does not …
INTRODUCTION MACHINE LEARNING - Stanford University
Department of Computer Science Stanford University Stanford, CA 94305 e-mail: nilsson@cs.stanford.edu ... 7.6 Relationships Between ILP and Decision Tree Induction . . . . . …
q 0 q sta CS103 rt 0 0, 1 - Stanford University
Computer science is the art of solving problems with computers. This is a broad definition that encom-passes an equally broad field. Within computer science, we find software engineering, …
