Böcker i Texts in Computer Science-serien

This highly accessible textbook teaches programming from first principles. In common with many programming courses, it uses Python as the introductory programming language before going on to use Java as the vehicle for more advanced programming concepts.The first part, which teaches Python, covers fundamental programming concepts, such as data types and control structures and functions. It introduces more complex data types such as lists and dictionaries and also deals with file handling. It introduces object-oriented concepts and ends with a case study bringing together all the topics of the first semester.  The second part uses Java to teach advanced concepts and centres around object-oriented programming, teaching key object-oriented concepts such as inheritance and polymorphism.  The semester again ends with an advanced case study bringing together all the topics of the second semester.Topics and features: Assumes no prior knowledge, and makes the transition from Python to Java a smooth process Features numerous exercises and also an illustrative case study for each language Examines procedural and object-oriented methodologies, as well as design principles Covers such advanced topics as interfaces and lambda expressions, exceptions and Collections Includes a chapter on graphics programming in Python using Tkinter  Introduces the latest Java technology for graphical interfaces, JavaFX Explains design concepts using UML notation Offering a gentle introduction to the field and assuming no prerequisite background, Programming in Two Semesters is the ideal companion to undergraduate modules in software development or programming.  In addition, it will serve as a strong primer for professionals looking to strengthen their knowledge of programming with these languages.

This textbook presents a systematic methodology for program development by using design recipes, i.e. a series of steps, each with a specific outcome, that takes a problem solver from a problem statement to a working and tested programmed solution. It introduces the reader to generative recursion, heuristic searching, accumulative recursion, tail recursion, iteration, mutation, loops, program correctness, and vectors. It uses video game development to make the content fun while at the same time teaching problem-solving techniques.The book is divided into four parts. Part I presents introductory material on basic problem solving and program design. It starts by reviewing the basic steps of a design recipe using structural recursion on a list. It then proceeds to review code refactoring-a common technique used to refine programs when a better or more elegant way is found to solve a problem-and introduces the reader to randomness. Next, Part II explores a new type of recursion called generative recursion. It navigates the reader through examples involving fractal image generation, efficient sorting, and efficient searching techniques such as binary, depth-first, and breadth-first search. Part III then explores a new type of recursion called accumulative (or accumulator) recursion. Examples used include finding a path in a graph, improving insertion sorting, and list-folding operations. Finally, Part IV explores mutation. To aid the reader in properly sequencing mutations it presents Hoare Logic and program correctness. In addition, it introduces vectors, vector processing, in-place operations, and circular data. Throughout the whole book complexity analysis and empirical experimentation is used to evaluate solutions.This textbook targets undergraduates at all levels as well as graduate students wishing to learn about program design. It details advanced types of recursion, a disciplined approach to the use of mutation, and illustrates the design process by developing a video game exploiting iterative refinement.

Computation theory is a discipline that uses mathematical concepts and tools to expose the nature of "e;computation"e; and to explain a broad range of computational phenomena: Why is it harder to perform some computations than others?  Are the differences in difficulty that we observe inherent, or are they artifacts of the way we try to perform the computations?  How does one reason about such questions?This unique textbook strives to endow students with conceptual and manipulative tools necessary to make computation theory part of their professional lives. The work achieves this goal by means of three stratagems that set its approach apart from most other texts on the subject.For starters, it develops the necessary mathematical concepts and tools from the concepts' simplest instances, thereby helping students gain operational control over the required mathematics. Secondly, it organizes development of theory around four "e;pillars,"e; enabling students to see computational topics that have the same intellectual origins in physical proximity to one another. Finally, the text illustrates the "e;big ideas"e; that computation theory is built upon with applications of these ideas within "e;practical"e; domains in mathematics, computer science, computer engineering, and even further afield.Suitable for advanced undergraduate students and beginning graduates, this textbook augments the "e;classical"e; models that traditionally support courses on computation theory with novel models inspired by "e;real, modern"e; computational topics,such as  crowd-sourced computing, mobile computing, robotic path planning, and volunteer computing.Arnold L. Rosenberg is Distinguished Univ. Professor Emeritus at University of Massachusetts, Amherst, USA. Lenwood S. Heath is Professor at Virgina Tech, Blacksburg, USA.            

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 textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success. Topics and features:Focuses on approaches supported by mathematical arguments, rather than sole computing experiencesInvestigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from themConsiders key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithmsExamines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problemAddresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrizationInvestigates the mathematical principles involves with natural language processing and computer visionKeeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire bookAlthough this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations ¿beyond¿ the sole computing experience.

This stimulating textbook presents a broad and accessible guide to the fundamentals of discrete mathematics, highlighting how the techniques may be applied to various exciting areas in computing. The text is designed to motivate and inspire the reader, encouraging further study in this important skill. Features: provides an introduction to the building blocks of discrete mathematics, including sets, relations and functions; describes the basics of number theory, the techniques of induction and recursion, and the applications of mathematical sequences, series, permutations, and combinations; presents the essentials of algebra; explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability; reviews the history of logic, discussing propositional and predicate logic, as well as advanced topics; examines the field of software engineering, describing formal methods; investigates probability and statistics.

Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence.

This textbook aims to help the reader develop an in-depth understanding of logical reasoning and gain knowledge of the theory of computation. Content-wise, this book focuses on the syntax, semantics and proof theory of various logics; This book is written for a high-level undergraduate course or a Master's course.

This textbook is about systematic problem solving and systematic reasoning using type-driven design. Divide and conquer is the process by which a large problem is broken into two or more smaller problems that are easier to solve and then the solutions for the smaller pieces are combined to create an answer to the problem.

Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs.

This textbook treats graph colouring as an algorithmic problem, with a strong emphasis on practical applications. and whether they can produce better solutions than other algorithms for certain types of graphs, and why. The introductory chapters explain graph colouring, complexity theory, bounds and constructive algorithms.

This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced third edition has been fully revised and expanded with new content on deep learning, scalarization methods, large-scale optimization algorithms, and collective decision-making algorithms. Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.

explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability;

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.

describes the details of cloud migration, the crucial role of monitoring in optimizing the cloud, and the basics of disaster recovery using cloud infrastructure. This technically rigorous yet simple-to-follow textbook is an ideal resource for graduate courses on cloud computing.

This comprehensive Guide to Web Development with Java introduces the readers to the three-tiered, Model-View-Controller architecture by using Spring JPA, JSPs, and Spring MVC controllers. These three technologies use Java, so that a student with a background in programming will be able to master them with ease, with the end result of being able to create web applications that use MVC, validate user input,and save data to a database.Topics and features:¿ Presents web development topics in an accessible, easy-to-follow style, focusing on core information first, and allowing the reader to gain basic understanding before moving forwards¿ Contains many helpful pedagogical tools for students and lecturers, such as questions and exercises at the end of each chapter, detailed illustrations, chapter summaries, and a glossary¿ Uses existing powerful technologies that are freely available on the web to speedup web development, such as Spring Boot, Spring MVC, Spring JPA, Hibernate, JSP, JSTL, and Java 1.8¿ Discusses HTML, HTML forms, and Cascading Style Sheets¿ Starts with the simplest technology for web development (JSP) and gradually introduces the reader to more complex topics¿ Introduces core technologies from the outset, such as the Model-View-Controller architecture¿ Includes examples for accessing common web services¿ Provides supplementary examples and tutorials

This book presents a broad overview of computer graphics (CG), its history, and the hardware tools it employs. examines advanced techniques in CG, including the nature and properties of light and color, graphics standards and file formats, and fractals;

Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.More than just a source of ΓÇ£recipes,ΓÇ¥ this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.Topics and features:Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized coursesIncorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented realityPresents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projectsIncludes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decadeProvides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and softwareSuitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.