Bücher der Reihe Texts in Computer Science

Here is a hands-on treatment of the subject of optimization, recommended for use by industry professionals, scientists, and students interested in optimization algorithms and their various applications. It provides a complete teaching package with MATLAB exercises and online solutions to end-of-chapter problems.

A dual purpose textbook. It is uniquely written to cover core material in the foundations of computing for graduate students in computer science and to provide an introduction to some more advanced topics for those intending further study in the field.

This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, and approximation algorithms and heuristics for such problems.

This classroom-tested book discusses artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks, covering ant colony optimization and probabilistic graphical models. Includes examples, illustrations and definitions throughout.

This book serves an introduction to data science, focusing on the skills and principles needed to build systems for collecting, analyzing, and interpreting data. As a discipline, data science sits at the intersection of statistics, computer science, and machine learning, but it is building a distinct heft and character of its own.In particular, the book stresses the following basic principles as fundamental to becoming a good data scientist: ΓÇ£Valuing Doing the Simple Things RightΓÇ¥, laying the groundwork of what really matters in analyzing data; ΓÇ£Developing Mathematical IntuitionΓÇ¥, so that readers can understand on an intuitive level why these concepts were developed, how they are useful and when they work best, and; ΓÇ£Thinking Like a Computer Scientist, but Acting Like a StatisticianΓÇ¥, following approaches which come most naturally to computer scientists while maintaining the core values of statistical reasoning. The book does not emphasize any particular language or suite of data analysis tools, but instead provides a high-level discussion of important design principles.This book covers enough material for an ΓÇ£Introduction to Data ScienceΓÇ¥ course at the undergraduate or early graduate student levels. A full set of lecture slides for teaching this course are available at an associated website, along with data resources for projects and assignments, and online video lectures.Other Pedagogical features of this book include: ΓÇ£War StoriesΓÇ¥ offering perspectives on how data science techniques apply in the real world; ΓÇ£False StartsΓÇ¥ revealing the subtle reasons why certain approaches fail; ΓÇ£Take-Home LessonsΓÇ¥ emphasizing the big-picture concepts to learn from each chapter; ΓÇ£Homework ProblemsΓÇ¥ providing a wide range of exercises for self-study; ΓÇ£Kaggle ChallengesΓÇ¥ from the online platform Kaggle; examples taken from the data science television show ΓÇ£The Quant ShopΓÇ¥, and; concluding notes in each tutorial chapter pointing readers to primary sources and additional references.

This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content.

This easy-to-follow textbook teaches Java programming from first principles, as well as covering design and testing methodologies. The text is divided into two parts. Each part supports a one-semester module, the first part addressing fundamental programming concepts, and the second part building on this foundation, teaching the skills required to develop more advanced applications.This fully updated and greatly enhanced fourth edition covers the key developments introduced in Java 8, including material on JavaFX, lambda expressions and the Stream API.Topics and features: begins by introducing fundamental programming concepts such as declaration of variables, control structures, methods and arrays; goes on to cover the fundamental object-oriented concepts of classes and objects, inheritance and polymorphism; uses JavaFX throughout for constructing event-driven graphical interfaces; includes advanced topics such as interfaces and lambda expressions, generics, collection classes and exceptions; explains file-handling techniques, packages, multi-threaded programs, socket programming, remote database access and processing collections using streams; includes self-test questions and programming exercises at the end of each chapter, as well as two illuminating case studies; provides additional resources at its associated website (simply go to springer.com and search for "Java in Two Semesters"), including a guide on how to install and use the NetBeans¿ Java IDE.Offering a gentle introduction to the field, assuming no prior knowledge of the subject, Java in Two Semesters is the ideal companion to undergraduate modules in software development or programming.

Explorations in Quantum Computing describes quantum computing in simple terms, and describes key technological hurdles that must be overcome in order to make quantum computers a reality. The text includes executable software simulations to illustrate the concepts and material.

This practically-focused study guide introduces the fundamentals of discrete mathematics through an extensive set of classroom-tested problems. Each chapter presents a concise introduction to the relevant theory, followed by a detailed account of common challenges and methods for overcoming these. The reader is then encouraged to practice solving such problems for themselves, by tackling a varied selection of questions and assignments of different levels of complexity.This updated second edition now covers the design and analysis of algorithms using Python, and features more than 50 new problems, complete with solutions.Topics and features: provides a substantial collection of problems and examples of varying levels of difficulty, suitable for both laboratory practical training and self-study; offers detailed solutions to each problem, applying commonly-used methods and computational schemes; introduces the fundamentals of mathematical logic, the theory of algorithms, Boolean algebra, graph theory, sets, relations, functions, and combinatorics; presents more advanced material on the design and analysis of algorithms, including Turing machines, asymptotic analysis, and parallel algorithms; includes reference lists of trigonometric and finite summation formulae in an appendix, together with basic rules for differential and integral calculus.This hands-on workbook is an invaluable resource for undergraduate students of computer science, informatics, and electronic engineering. Suitable for use in a one- or two-semester course on discrete mathematics, the text emphasizes the skills required to develop and implement an algorithm in a specific programming language.

The challenges of problems from international programming competitions are an effective way to improve your algorithmic and coding skills and understanding. This volume uses international programming competition-type problems to motivate the study of algorithms, programming, and other topics in computer science.

This book surveys theoretical computer science, presenting fundamental concepts and results. Updated and revised, the new edition includes two new chapters on nonuniform complexity, circuit complexity and parallel complexity, and randomized complexity.

This book describes the key concepts, principles and implementation options for creating high-assurance cloud computing solutions, beginning with a broad technical overview, and moving through the challenges of reliability and fault-tolerance.

In its revised fifth edition, this book covers ethical, social and policy challenges arising from the convergence of computing and telecommunication and the spread of mobile information devices. Asks important questions about the impact of new technologies.

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an ¿Introduction to Data Science¿ course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinctheft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains ¿War Stories,¿ offering perspectives on how data science applies in the real worldIncludes ¿Homework Problems,¿ providing a wide range of exercises and projects for self-studyProvides a complete set of lecture slides and online video lectures at www.data-manual.comProvides ¿Take-Home Lessons,¿ emphasizing the big-picture concepts to learn from each chapterRecommends exciting ¿Kaggle Challenges¿ from the online platform KaggleHighlights ¿False Starts,¿ revealing the subtle reasons why certain approaches failOffers examples taken from the data science television show ¿The Quant Shop¿ (www.quant-shop.com)

Since the introduction of Hoares' Communicating Sequential Process (CSP) notation, powerful new tools have transformed CSP into a practical way of describing industrial-sized problems. This book presents the basic CSP knowledge needed to utilize those tools.

This text covers the state of the art in multivariate algorithmics and complexity, a vital field with countless applications in modern computing. It describes the latest methods of proving parameterized tractability, including powerful lower-bound techniques.

This effective introduction to the key concepts in computational social science includes formal definitions and a glossary, covers topics such as information extraction, social networks and complexity theory, and discusses a range of methodological tools.

Written by two experts in the field, this is the only comprehensive and unified treatment of the central ideas and applications of Kolmogorov complexity. The book presents a thorough treatment of the subject with a wide range of illustrative applications.

Revised and updated, Specification of Software Systems builds upon the original focus on software specification with added emphasis on the practice of formal methods for specification and verification. The text covers a wide range of formal specification techniques.

This comprehensive book covers the fundamental concepts of formal languages and compilation. It presents the subject in a minimalist way, using illustrated examples throughout and providing pedagogical tools, such as slides for lecturers via website.

In its revised and updated edition, this book now offers coverage of such multimedia topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks and their attendant technologies. Includes study exercises.

In recent years, model checking has become an essential technique for the formal verification of systems. It is perfectly suited for an advanced undergraduate or graduate class in formal verification and will serve as a valuable resource to practitioners of formal methods.

This book presents current methods for dealing with software reliability, illustrating the advantages and disadvantages of each method. This reference will serve as an introduction to formal methods and techniques and will be a source for learning about various ways to enhance software reliability.

This book explores one of the most common approaches to program verification, known as the assertional approach. Case studies supplied throughout the book demonstrate the use of the proof systems and formally verify solutions to classical problems.

Using a HDL, Verilog in this case, rather than static circuit diagrams is a big deviation from traditional books on computer architecture.

Introduced forty years ago, relational databases proved unusually succe- ful and durable. As a result, specialized database systems now proliferate trying to capture various pieces of the database market. The information retrieval systems were followed by database systems, which added even more structure to the data and made querying easier.

This book introduces the fundamental concepts, models, techniques, and results that form the basic paradigms of computing. It examines all the essential topics in formal languages, automata, computability, and complexity.

This is an introductory textbook and guide to the rapidly evolving field of predictive text mining. There are chapter summaries, historical and bibliographic remarks, and classroom-tested exercises for each chapter. Descriptive case studies are also included.

This book provides a thorough treatment of image processing with an emphasis on those aspects most used in computer graphics. The authors concentrate on describing and analyzing the underlying concepts rather than on presenting algorithms or pseudocode.

There are many kinds of books on formal logic. It does contain a thorough presentation of formal logic and many proof techniques, and as such it contains all the material one would expect to find in a course in formal logic covering completeness but, not incompleteness issues.