Written from a computer science perspective by three leading experts in the field, 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. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    Blends academic precision and practical experience in an authoritative introduction to basic principles of digital design and practical requirements. With over 30 years of experience in both industrial and university settings, the author covers the most widespread logic design practices while building a solid foundation of theoretical and engineering principles for students to use as they go forward in this fast moving field.

    Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.

    Because of the way the book is organized, it may be used at a variety of levels of rigor. For example, if matrix algebra is used, theoretical exercises may be omitted. A CD of data sets is provided with the text.

    Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by "doing," writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant "xkcd" comics. Chapters: "The Function," "The Field," "The Vector," "The Vector Space," "The Matrix," "The Basis," "Dimension," "Gaussian Elimination," "The Inner Product," "Special Bases," "The Singular Value Decomposition," "The Eigenvector," "The Linear Program"

    Aimed at accountants or readers of other career paths, this book helps them build their decision making skills and understand how to use accounting information to make quality business decisions.

    This book provides an applications-oriented introduction to multivariate data analysis for the non-statistician, by focusing on the fundamental concepts that affect the use of specific techniques.

    A number of the key figures in the new edition are revised to provide a more inviting and informative treatment. The figures are broken into component parts with supporting commentary so that they can more readily see the key ideas. Material from The Flying Circus is incorporated into the chapter opener puzzlers, sample problems, examples and end-of-chapter problems to make the subject more engaging. Checkpoints enable them to check their understanding of a question with some reasoning based on the narrative or sample problem they just read. Sample Problems also demonstrate how engineers can solve problems with reasoned solutions.

    The book teaches a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.

    In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.

    It can serve both as an excellent introduction for the graduate student with some background in probability theory or as a valuable reference for the practicing ommunication system engineer. For both communities, the treatment is clear and well presented." - Andrew Viterbi, The Viterbi Group Master every key digital communications technology, concept, and technique. Digital Communications, Second Edition is a thoroughly revised and updated edition of the field's classic, best-selling introduction. With remarkable clarity, Dr. Bernard Sklar introduces every digital communication technology at the heart of today's wireless and Internet revolutions, providing a unified structure and context for understanding them -- all without sacrificing mathematical precision. Sklar begins by introducing the fundamentals of signals, spectra, formatting, and baseband transmission. Next, he presents practical coverage of virtually every contemporary modulation, coding, and signal processing technique, with numeric examples and step-by-step implementation guidance. Coverage includes: Signals and processing steps: from information source through transmitter, channel, receiver, and information sinkKey tradeoffs: signal-to-noise ratios, probability of error, and bandwidth expenditureTrellis-coded modulation and Reed-Solomon codes: what's behind the mathSynchronization and spread spectrum solutionsFading channels: causes, effects, and techniques for withstanding fadingThe first complete how-to guide to turbo codes: squeezing maximum performance out of digital connectionsImplementing encryption with PGP, the de facto industry standard Whether you're building wireless systems, xDSL, fiber or coax-based services, satellite networks, or Internet infrastructure, Sklar presents the theory and the practical implementation details you need. With nearly 500 illustrations and 300 problems and exercises, there's never been a faster way to master advanced digital communications. CD-ROM INCLUDED The CD-ROM contains a complete educational version of Elanix' SystemView DSP design software, as well as detailed notes for getting started, a comprehensive DSP tutorial, and over 50 additional communications exercises.

    Offering an approach with a visual/graphical emphasis, this text offers a number of examples on the premise that students learn best by doing. This book features an emphasis on interpretation of results and critical thinking over calculations.

    This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and grammar correction, information extraction, search engines, machine translation, and the creation of spoken-language dialog agents. The following distinguishing features make the text both an introduction to the field and an advanced reference guide.- UNIFIED AND COMPREHENSIVE COVERAGE OF THE FIELDCovers the fundamental algorithms of each field, whether proposed for spoken or written language, whether logical or statistical in origin.- EMPHASIS ON WEB AND OTHER PRACTICAL APPLICATIONSGives readers an understanding of how language-related algorithms can be applied to important real-world problems.- EMPHASIS ON SCIENTIFIC EVALUATIONOffers a description of how systems are evaluated with each problem domain.- EMPERICIST/STATISTICAL/MACHINE LEARNING APPROACHES TO LANGUAGE PROCESSINGCovers all the new statistical approaches, while still completely covering the earlier more structured and rule-based methods.

    instructional book for students or anyone who needs to learn business communications.