This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal.

In this substantive yet accessible book, pioneering software designer Alexander Stepanov and his colleague Daniel Rose illuminate the principles of generic programming and the mathematical concept of abstraction on which it is based, helping you write code that is both simpler and more powerful.
If you’re a reasonably proficient programmer who can think logically, you have all the background you’ll need. Stepanov and Rose introduce the relevant abstract algebra and number theory with exceptional clarity. They carefully explain the problems mathematicians first needed to solve, and then show how these mathematical solutions translate to generic programming and the creation of more effective and elegant code. To demonstrate the crucial role these mathematical principles play in many modern applications, the authors show how to use these results and generalized algorithms to implement a real-world public-key cryptosystem.
As you read this book, you’ll master the thought processes necessary for effective programming and learn how to generalize narrowly conceived algorithms to widen their usefulness without losing efficiency. You’ll also gain deep insight into the value of mathematics to programming–insight that will prove invaluable no matter what programming languages and paradigms you use.
You will learn about
How to generalize a four thousand-year-old algorithm, demonstrating indispensable lessons about clarity and efficiency
Ancient paradoxes, beautiful theorems, and the productive tension between continuous and discrete
A simple algorithm for finding greatest common divisor (GCD) and modern abstractions that build on it
Powerful mathematical approaches to abstraction
How abstract algebra provides the idea at the heart of generic programming
Axioms, proofs, theories, and models: using mathematical techniques to organize knowledge about your algorithms and data structures
Surprising subtleties of simple programming tasks and what you can learn from them
How practical implementations can exploit theoretical knowledge

....The most comprehensive guide to designing practical and efficient algorithms....
Written by a well-known algorithms researcher who received the IEEE Computer Science and Engineering Teaching Award, this new edition of The Algorithm Design Manual is an essential learning tool for students needing a solid grounding in algorithms, as well as a special text/reference for professionals who need an authoritative and insightful guide. Professor Skiena is also author of the popular Springer text, Programming Challenges: The Programming Contest Training Manual.

Describes basic programming principles and their step-by- step applications.Numerous examples are included.

Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices. In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior. The underlying data structure of these and many other quantitative studies of applied sciences is multivariate. Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. All chapters have exercises that highlight applications in different fields. The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features A new Chapter on Regression Models has been addedAll numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.

This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

Presents a complete guide to ANSI standard C language programming. Written by the developers of C, this new version helps readers keep up with the finalized ANSI standard for C while showing how to take advantage of C's rich set of operators, economy of expression, improved control flow, and data structures. This 2nd edition has been completely rewritten with additional examples and problem sets to clarify the implementation of difficult language constructs. 7 x 9 1/4.

This is the 2nd edition of "Speech and Language Processing, 2000" (http://www.douban.com/subject/1810715/).
An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. Builds each chapter around one or more worked examples demonstrating the main idea of the chapter, usingthe examples to illustrate the relative strengths and weaknesses of various approaches. Adds coverage of statistical sequence labeling, information extraction, question answering and summarization, advanced topics in speech recognition, speech synthesis. Revises coverage of language modeling, formal grammars, statistical parsing, machine translation, and dialog processing. A useful reference for professionals in any of the areas of speech and language processing.

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. * Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry * A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking * More than 70 algorithms are described in sufficient detail to implement * More than 350 full-color illustrations amplify the text * The treatment is self-contained, including all of the background mathematics * Additional resources at www.computervisionmodels.com

Bridges the gap between theoretical and computational aspects of prime numbers Exercise sections are a goldmine of interesting examples, pointers to the literature and potential research projects Authors are well-known and highly-regarded in the field

For undergraduate-level courses in Signals and Systems. This comprehensive exploration of signals and systems develops continuous-time and discrete-time concepts/methods in parallel -- highlighting the similarities and differences -- and features introductory treatments of the applications of these basic methods in such areas as filtering, communication, sampling, discrete-time processing of continuous-time signals, and feedback. Relatively self-contained, the text assumes no prior experience with system analysis, convolution, Fourier analysis, or Laplace and z-transforms.

Regular expressions remain a difficult part of the puzzle when learning how to program. Commonly used for sifting through large chunks of text, regexes are incredibly powerful although they may appear daunting to the newcomer. And variations among languages and environments make them even harder to master. Loaded with examples, this introductory guide walks beginners step-by-step through the fundamentals of regular expressions, and helps you decipher complex patterns. * Break down regular expressions into comprehensible parts * Learn common usage patterns through simple, easy-to-follow examples * Discover how finding unique patterns can help you avoid repetitive, hand-editing of text * Use common command-line tools such as grep and sed * Compare how regular expressions are implemented in different languages and environments

Regular expressions are an extremely powerful tool for manipulating text and data. They are now standard features in a wide range of languages and popular tools, including Perl, Python, Ruby, Java, VB.NET and C# (and any language using the .NET Framework), PHP, and MySQL.
If you don't use regular expressions yet, you will discover in this book a whole new world of mastery over your data. If you already use them, you'll appreciate this book's unprecedented detail and breadth of coverage. If you think you know all you need to know about regular expressions, this book is a stunning eye-opener.
As this book shows, a command of regular expressions is an invaluable skill. Regular expressions allow you to code complex and subtle text processing that you never imagined could be automated. Regular expressions can save you time and aggravation. They can be used to craft elegant solutions to a wide range of problems. Once you've mastered regular expressions, they'll become an invaluable part of your toolkit. You will wonder how you ever got by without them.
Yet despite their wide availability, flexibility, and unparalleled power, regular expressions are frequently underutilized. Yet what is power in the hands of an expert can be fraught with peril for the unwary. Mastering Regular Expressions will help you navigate the minefield to becoming an expert and help you optimize your use of regular expressions.
Mastering Regular Expressions, Third Edition, now includes a full chapter devoted to PHP and its powerful and expressive suite of regular expression functions, in addition to enhanced PHP coverage in the central "core" chapters. Furthermore, this edition has been updated throughout to reflect advances in other languages, including expanded in-depth coverage of Sun's java.util.regex package, which has emerged as the standard Java regex implementation. Topics include:
A comparison of features among different versions of many languages and tools
How the regular expression engine works
Optimization (major savings available here!)
Matching just what you want, but not what you don't want
Sections and chapters on individual languages
Written in the lucid, entertaining tone that makes a complex, dry topic become crystal-clear to programmers, and sprinkled with solutions to complex real-world problems, Mastering Regular Expressions, Third Edition offers a wealth information that you can put to immediate use.
Reviews of this new edition and the second edition:
"There isn't a better (or more useful) book available on regular expressions."
--Zak Greant, Managing Director, eZ Systems
"A real tour-de-force of a book which not only covers the mechanics of regexes in extraordinary detail but also talks about efficiency and the use of regexes in Perl, Java, and .NET...If you use regular expressions as part of your professional work (even if you already have a good book on whatever language you're programming in) I would strongly recommend this book to you."
--Dr. Chris Brown, Linux Format
"The author does an outstanding job leading the reader from regex novice to master. The book is extremely easy to read and chock full of useful and relevant examples...Regular expressions are valuable tools that every developer should have in their toolbox. Mastering Regular Expressions is the definitive guide to the subject, and an outstanding resource that belongs on every programmer's bookshelf. Ten out of Ten Horseshoes."
--Jason Menard, Java Ranch