Foundations of Computer Science (5版)

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Based on the Association for Computing Imagery model curriculum guidelines, Foundations of Computer Science gives students a bird's eye view of Computer Science. This easy-to-read and highly navigable text will help students quickly learn all the fundamentals of computer science they need on their course and to improve their employability prospects. Updated to cover all the latest technologies and changes to course requirements, this fifth edition features new chapters on the efficiency of algorithms, computer graphics, a complete revision on computer networks and the internet, and data security. Material on ethical issues, including intellectual property, has also been expanded.

計算機概論 (13版)

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計算機概論 ISBN13：9789867696359 替代書名：Brookshear 13/e 出版社：滄海 作者：J. Glenn Brookshear;Dennis Brylow-著 譯者：林信男 裝訂／頁數：平裝／680頁 規格：26cm*19cm*3cm (高/寬/厚) 版次：13 出版日：2020/08/01 中國圖書分類：電腦科學 內容簡介 1.本書涵蓋層面很廣，但是淺顯易懂，作者以循序漸進的方式說明了許多計算機概論裡一些複雜的概念，並且賦予大量的例子和圖表輔以說明。 2.本書很適合資訊專科或非資訊專科的學生，閱讀起來幾乎沒有什麼難度，只 要看了本書，對計算機科學相關的許多議題都會有清楚的概念。 3.本書也提供了大量的習題，讓讀者更深入瞭解計算機科學。 4.本書也讓人可以進一步地思考計算機科學在社會層面、法律層面以及倫理層面的影響，不僅在專業上提供很清楚的論述，也關心許多社會議題；這是一本寫得很好的計算機概論。 目錄 第 0 章 緒論 第 1 章 資料儲存存 第 2 章 資料處理 第 3 章 作業系統 第 4 章 網路通訊與網際網路 第 5 章 演算法 第 6 章 程式語言 第 7 章 軟體工程 第 8 章 資料抽象化 第 9 章 資料庫系統 第 10 章 電腦繪圖 第 11 章 人工智慧 第 12 章 計算理論 附錄 A ASCII B 處理二的補數表示法的電路 C Vole：一個簡單的機器語言 D 高階程式語言 E 迭代與遞迴結構的等價關係

計算機概論 (5版)

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【簡介】 電腦科學是一門不斷發展和進步的年輕學科，在日常生活中扮演著重要的角色，且在未來也將持續影響我們的生活。本書可作為專業人士自學指南，也可以是一個學期或一個學季的教科書，內容包括電腦科學的全部領域：電腦及資訊、硬體與軟體、資訊組織型態、程式設計、演算法、資訊安全等。本書著重概念的建立，並以多達420張圖形、豐富的範例、演算法、習題等來加強讀者的學習效果。 【目錄】 1 簡介 2 數目系統 3 資料的儲存 4 資料運算 5 計算機組織 6 電腦網路 7 作業系統 8 演算法 9 演算法效率 10 程式語言 11 軟體工程 12 資料結構 13 抽象資料型態 14 檔案結構 15 資料庫 16 資料壓縮 17 資訊安全 18 計算理論 19 計算機圖學 20 人工智慧 21 社群媒體 22 社會和道德問題 附錄 A 萬國碼 B 統一塑模語言 C 虛擬程式碼 D 結構圖 E 符號大小整數的加法與減法 F 實數的加法與減法

微積分 (9版)

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Calculus 9/e Metric Version +作者：Stewart +年份：2021 年9 版 +ISBN：9780357113462 +書號：MA0475HC +規格：精裝/彩色 +頁數：1440 +出版商：Cengage 原文本目錄 1. FUNCTIONS AND LIMITS. 2. DERIVATIVES. 3. APPLICATIONS OF DIFFERENTIATION. 4. INTEGRALS. 5. APPLICATIONS OF INTEGRATION. 6. INVERSE FUNCTIONS: EXPONENTIAL, LOGARITHMIC, AND INVERSE TRIGONOMETRIC FUNCTIONS. 7. TECHNIQUES OF INTEGRATION. 8. FURTHER APPLICATIONS OF INTEGRATION. 9. DIFFERENTIAL EQUATIONS. 10. PARAMETRIC EQUATIONS AND POLAR COORDINATES. 11. SEQUENCES, SERIES, AND POWER SERIES. 12. VECTORS AND THE GEOMETRY OF SPACE. 13. VECTOR FUNCTIONS. 14. PARTIAL DERIVATIVES. 15. MULTIPLE INTEGRALS. 16. VECTOR CALCULUS. APPENDIXES. A Numbers, Inequalities, and Absolute Values. B Coordinate Geometry and Lines. C Graphs of Second-Degree Equations. D Trigonometry. E Sigma Notation. F Proofs of Theorems. G Answers to Odd-Numbered Exercises. 翻譯本目錄 第一章 函數與極限 第二章 導數 第三章 微分的應用 第四章 積分 第五章 積分的應用 第六章 反函數 第七章 積分技巧 第八章 數列、級數與冪級數 第九章 空間的向量與幾何 第十章 偏導數 第十一章 多重積分 附錄

Management Information Systems: Managing the Digital Firm (17版)

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Management Information Systems: Managing the Digital Firm 17/e +作者：Laudon +年份：2022 年17 版 +ISBN：9781292403281 +書號：CS0440PC +規格：平裝/彩色 +頁數：648 +出版商：Pearson(Asia) 目錄 PART ONE Organizations, Management, and the Networked Enterprise Chapter 1 Information Systems in Global Business Today Chapter 2 Global E-business and Collaboration Chapter 3 Information Systems, Organizations, and Strategy Chapter 4 Ethical and Social Issues in Information Systems PART TWO Information Technology Infrastructure Chapter 5 IT Infrastructure and Emerging Technologies Chapter 6 Foundations of Business Intelligence: Databases and Information Management Chapter 7 Telecommunications, the Internet, and Wireless Technology Chapter 8 Securing Information Systems PART THREE Key System Applications for the Digital Age Chapter 9 Achieving Operational Excellence and Customer Intimacy: Enterprise Applications Chapter 10 E-commerce: Digital Markets, Digital Goods Chapter 11 Managing Knowledge and Artificial Intelligence Chapter 12 Enhancing Decision Making PART FOUR Building and Managing Systems Chapter 13 Building Information Systems Chapter 14 Making the Business Case for Information Systems and Managing Projects Chapter 15 Managing Global Systems

Vector Mechanics for Engineers: Statics 11/e (Annotated Edition) (11版)

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Vector Mechanics for Engineers: Statics 11/e (Annotated Edition) 導讀本 作者：Ferdinand Beer 著；莊嘉揚 導讀 ISBN：9789863411895 版次：11 年份：2016 出版商：東華書局 頁數/規格：656頁/平裝雙色 本書特色 本書第 1 版於 1956 年出版，之後陸續修訂改版至今已第 11 版。由於本書內容嚴謹、文字簡潔、圖片豐富、編排精美，每章均附有百道習題可供練習，因此一直為國內外大專院校講授靜力學的主要教材之一。 靜力學為力學領域的入門課程，為許多工程科系的必修課。內容延續高中物理的基礎力學內容，再日入更有系統、更深入的探討，強調自由體圖的概念，並引入向量表示法求解工程中常見結構的力平衡問題。然而筆者發現大一新生修習本課時常因為熟悉英文慣用法和專有名詞，無法和高中學過的中文概念對應，以致筆者上課時往往必須先表最專有名詞和重要觀念的中英文對照。本導讀版將每章複習與摘要譯成中文，希望讀者能很快抓住每章的重點並清楚專有名詞的中英文對照，學習得更有效率。 目　錄 1 Introduction 2 Statics of Particles 3 Rigid Bodies: Equivalent Systems of Forces 4 Equilibrium of Rigid Bodies 5 Distributed Forces: Centroids and Centers of Gravity 6 Analysis of Structures 7 Internal Forces and Moments 8 Friction 9 Distributed Forces: Moments of Inertia 10 Method of Virtual Work

Calculus Metric Version （Custom Solutions） (9版)

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【簡介】 【目錄】 1. FUNCTIONS AND LIMITS. 2. DERIVATIVES. 3. APPLICATIONS OF DIFFERENTIATION. 4. INTEGRALS. 5. APPLICATIONS OF INTEGRATION. 6. INVERSE FUNCTIONS: EXPONENTIAL, LOGARITHMIC, AND INVERSE TRIGONOMETRIC FUNCTIONS. 7. TECHNIQUES OF INTEGRATION. 8. FURTHER APPLICATIONS OF INTEGRATION. 9. DIFFERENTIAL EQUATIONS. 10. PARAMETRIC EQUATIONS AND POLAR COORDINATES. 11. SEQUENCES, SERIES, AND POWER SERIES. 12. VECTORS AND THE GEOMETRY OF SPACE. 13. VECTOR FUNCTIONS. 14. PARTIAL DERIVATIVES. 15. MULTIPLE INTEGRALS. 16. VECTOR CALCULUS. APPENDIXES. A Numbers, Inequalities, and Absolute Values. B Coordinate Geometry and Lines. C Graphs of Second-Degree Equations. D Trigonometry. E Sigma Notation. F Proofs of Theorems. G Answers to Odd-Numbered Exercises.

An Introduction to the Theory of Computer Science Language and Machines (3版)

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The third edition of Languages and Machines: An Introduction to the Theory of Computer Science provides readers with a mathematically sound presentation of the theory of computer science at a level suitable for junior and senior level computer science majors. The theoretical concepts and associated mathematics are made accessible by a "learn as you go" approach that develops an intuitive understanding of the concepts through numerous examples and illustrations. In this edition the presentation has been enhanced by increasing the number of examples, expanding the selection of topics particularly in the area of computational complexity, and providing a flexible format giving instructors the ability to design their courses that concentrate on specific areas such as automata theory, computability theory, or computational complexity.

Probability with R: An Introduction with Computer Science Applications (2版)

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【簡介】 Provides a comprehensive introduction to probability with an emphasis on computing-related applications This self-contained new and extended edition outlines a first course in probability applied to computer-related disciplines. As in the first edition, experimentation and simulation are favoured over mathematical proofs. The freely down-loadable statistical programming language R is used throughout the text, not only as a tool for calculation and data analysis, but also to illustrate concepts of probability and to simulate distributions. The examples in Probability with R: An Introduction with Computer Science Applications, Second Edition cover a wide range of computer science applications, including: testing program performance; measuring response time and CPU time; estimating the reliability of components and systems; evaluating algorithms and queuing systems. Chapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. This second edition includes: improved R code throughout the text, as well as new procedures, packages and interfaces; updated and additional examples, exercises and projects covering recent developments of computing; an introduction to bivariate discrete distributions together with the R functions used to handle large matrices of conditional probabilities, which are often needed in machine translation; an introduction to linear regression with particular emphasis on its application to machine learning using testing and training data; a new section on spam filtering using Bayes theorem to develop the filters; an extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud; use of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem. The book is supplemented with a Wiley Book Companion Site featuring data and solutions to exercises within the book. Primarily addressed to students of computer science and related areas, Probability with R: An Introduction with Computer Science Applications, Second Edition is also an excellent text for students of engineering and the general sciences. Computing professionals who need to understand the relevance of probability in their areas of practice will find it useful. 【目錄】 Preface to the Second Edition xiii Preface to the First Edition xvii Acknowledgments xxi About the Companion Website xxiii I The R Language 1 1 Basics of R 3 1.1 What is R? 3 1.2 Installing R 4 1.3 R Documentation 4 1.4 Basics 5 1.5 Getting Help 6 1.6 Data Entry 7 1.7 Missing Values 11 1.8 Editing 12 1.9 Tidying Up 12 1.10 Saving and Retrieving 13 1.11 Packages 13 1.12 Interfaces 14 1.13 Project 16 2 Summarizing Statistical Data 17 2.1 Measures of Central Tendency 17 2.2 Measures of Dispersion 21 2.3 Overall Summary Statistics 24 2.4 Programming in R 25 2.5 Project 30 3 Graphical Displays 31 3.1 Boxplots 31 3.2 Histograms 36 3.3 Stem and Leaf 40 3.4 Scatter Plots 40 3.5 The Line of Best Fit 43 3.6 Machine Learning and the Line of Best Fit 44 3.7 Graphical Displays Versus Summary Statistics 49 3.8 Projects 53 II Fundamentals of Probability 55 4 Probability Basics 57 4.1 Experiments, Sample Spaces, and Events 58 4.2 Classical Approach to Probability 61 4.3 Permutations and Combinations 64 4.4 The Birthday Problem 71 4.5 Balls and Bins 76 4.6 R Functions for Allocation 79 4.7 Allocation Overload 81 4.8 Relative Frequency Approach to Probability 83 4.9 Simulating Probabilities 84 4.10 Projects 89 5 Rules of Probability 91 5.1 Probability and Sets 91 5.2 Mutually Exclusive Events 92 5.3 Complementary Events 93 5.4 Axioms of Probability 94 5.5 Properties of Probability 96 6 Conditional Probability 104 6.1 Multiplication Law of Probability 107 6.2 Independent Events 108 6.3 Independence of More than Two Events 110 6.4 The Intel Fiasco 113 6.5 Law of Total Probability 115 6.6 Trees 118 6.7 Project 123 7 Posterior Probability and Bayes 124 7.1 Bayes’ Rule 124 7.2 Hardware Fault Diagnosis 131 7.3 Machine Learning and Classification 132 7.4 Spam Filtering 135 7.5 Machine Translation 137 8 Reliability 142 8.1 Series Systems 142 8.2 Parallel Systems 143 8.3 Reliability of a System 143 8.4 Series–Parallel Systems 150 8.5 The Design of Systems 153 8.6 The General System 158 III Discrete Distributions 161 9 Introduction to Discrete Distributions 163 9.1 Discrete Random Variables 163 9.2 Cumulative Distribution Function 168 9.3 Some Simple Discrete Distributions 170 9.4 Benford’s Law 174 9.5 Summarizing Random Variables: Expectation 175 9.6 Properties of Expectations 180 9.7 Simulating Discrete Random Variables and Expectations 183 9.8 Bivariate Distributions 187 9.9 Marginal Distributions 189 9.10 Conditional Distributions 190 9.11 Project 194 10 The Geometric Distribution 196 10.1 Geometric Random Variables 198 10.2 Cumulative Distribution Function 203 10.3 The Quantile Function 207 10.4 Geometric Expectations 209 10.5 Simulating Geometric Probabilities and Expectations 210 10.6 Amnesia 217 10.7 Simulating Markov 219 10.8 Projects 224 11 The Binomial Distribution 226 11.1 Binomial Probabilities 227 11.2 Binomial Random Variables 229 11.3 Cumulative Distribution Function 233 11.4 The Quantile Function 235 11.5 Reliability: The General System 238 11.6 Machine Learning 241 11.7 Binomial Expectations 245 11.8 Simulating Binomial Probabilities and Expectations 248 11.9 Projects 254 12 The Hypergeometric Distribution 255 12.1 Hypergeometric Random Variables 257 12.2 Cumulative Distribution Function 260 12.3 The Lottery 262 12.4 Hypergeometric or Binomial? 266 12.5 Projects 273 13 The Poisson Distribution 274 13.1 Death by Horse Kick 274 13.2 Limiting Binomial Distribution 275 13.3 Random Events in Time and Space 281 13.4 Probability Density Function 283 13.5 Cumulative Distribution Function 287 13.6 The Quantile Function 289 13.7 Estimating Software Reliability 290 13.8 Modeling Defects in Integrated Circuits 292 13.9 Simulating Poisson Probabilities 293 13.10 Projects 298 14 Sampling Inspection Schemes 299 14.1 Introduction 299 14.2 Single Sampling Inspection Schemes 300 14.3 Acceptance Probabilities 301 14.4 Simulating Sampling Inspection Schemes 303 14.5 Operating Characteristic Curve 308 14.6 Producer’s and Consumer’s Risks 310 14.7 Design of Sampling Schemes 311 14.8 Rectifying Sampling Inspection Schemes 315 14.9 Average Outgoing Quality 316 14.10 Double Sampling Inspection Schemes 318 14.11 Average Sample Size 319 14.12 Single Versus Double Schemes 320 14.13 Projects 324 IV Continuous Distributions 325 15 Introduction to Continuous Distributions 327 15.1 Introduction to Continuous Random Variables 328 15.2 Probability Density Function 328 15.3 Cumulative Distribution Function 331 15.4 The Uniform Distribution 332 15.5 Expectation of a Continuous Random Variable 336 15.6 Simulating Continuous Variables 338 16 The Exponential Distribution 341 16.1 Modeling Waiting Times 341 16.2 Probability Density Function of Waiting Times 342 16.3 Cumulative Distribution Function 344 16.4 Modeling Lifetimes 347 16.5 Quantiles 349 16.6 Exponential Expectations 351 16.7 Simulating Exponential Probabilities and Expectations 353 16.8 Amnesia 356 16.9 Simulating Markov 360 16.10 Project 369 17 Queues 370 17.1 The Single Server Queue 370 17.2 Traffic Intensity 371 17.3 Queue Length 372 17.4 Average Response Time 376 17.5 Extensions of the M/M/1 Queue 378 17.6 Project 382 18 The Normal Distribution 383 18.1 The Normal Probability Density Function 385 18.2 The Cumulative Distribution Function 387 18.3 Quantiles 389 18.4 The Standard Normal Distribution 391 18.5 Achieving Normality: Limiting Distributions 394 18.6 Projects 405 19 Process Control 407 19.1 Control Charts 407 19.2 Cusum Charts 411 19.3 Charts for Defective Rates 412 19.4 Project 416 V Tailing Off 417 20 The Inequalities of Markov and Chebyshev 419 20.1 Markov’s Inequality 420 20.2 Algorithm Runtime 426 20.3 Chebyshev’s Inequality 427 Appendix A: Data: Examination Results 433 Appendix B: The Line of Best Fit: Coefficient Derivations 437 Appendix C: Variance Derivations 440 Appendix D: Binomial Approximation to the Hypergeometric 446 Appendix E: Normal Tables 448 Appendix F: The Inequalities of Markov and Chebyshev 450 Index to R Commands 453 Index 457 Postface

AP Computer Science Principles Premium, 2026 (1版)

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【簡介】 ＂Be prepared for exam day with Barron’s. Trusted content from AP experts! Barron’s AP Computer Science Principles Premium, 2023 includes in-depth content review and online practice. It’s the only book you’ll need to be prepared for exam day.＂--

Princeton Review AP Computer Science Principles Premium Prep (4版)

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【簡介】 PREMIUM PRACTICE FOR A PERFECT 5! Ace the newly-digital AP Computer Science Principles Exam with this comprehensive study guide--including 5 practice tests with answer explanations, timed online practice, and thorough content review.The Princeton Review ExpertiseStudy with prep and practice written entirely by AP educatorsLearn test-taking strategies backed by 40+ years of test prep success Focused, supportive lessons designed and perfected by experts Everything You Need for a High ScoreA step-by-step guide on how to better your score with this bookComprehensive review of all topics on the newest digital exam, including the Create Performance TaskCustomize a study plan and target areas of improvement by using our diagnostic answer keyDrills in each content review chapter, sample question walk-throughs, and detailed pseudocode explanationsExclusive online digital flashcards to hone essential conceptsPremium Practice for AP Excellence5 full-length practice tests (3 in the book, 2 online)Detailed answer explanations to help you learn from mistakesOnline tests provided as both digital versions (with timer option to simulate exam experience), and as downloadable PDFs (with interactive elements mimicking the exam interface)Get more via your online student tools--including a list of key terms and concepts, study plans, and exam updates