Objective-C is a powerful, object-oriented extension of C, making this book the perfect follow-up to Dave Mark's bestselling Learn C on the Mac, Mac OS X Edition. Whether you're an experienced C programmer or you're coming from a different language such as C++ or Java, leading Mac experts Mark Dalrymple and Scott Knaster show you how to harness the powers of Objective-C in your applications!A complete course on the basics of Objective-C using Apple's free Xcode tools An introduction to object-oriented programming Comprehensive coverage of inheritance, composition, object initialization, categories, protocols, memory management, and organizing source files A brief tour of Cocoa's foundation framework and AppKit A helpful "learning curve" guide for non-C developers

    Theoretical, yet applied. Statistics for Business and Economics, Eleventh Edition, gives you the best of both worlds. Using a rich array of applications from a variety of industries, McClave/Sincich/Benson clearly demonstrates how to use statistics effectively in a business environment.The book focuses on developing statistical thinking so the reader can better assess the credibility and value of inferences made from data. As consumers and future producers of statistical inferences, readers are introduced to a wide variety of data collection and analysis techniques to help them evaluate data and make informed business decisions. As with previous editions, this revision offers an abundance of applications with many new and updated exercises that draw on real business situations and recent economic events. The authors assume a background of basic algebra.

    Building on the successful top-down approach of previous editions, this fourth edition continues with an early emphasis on application-layer paradigms and application programming interfaces, encouraging a hands-on experience with protocols and networking concepts.

    Max 'Vision' Butler was a white-hat hacker and a celebrity throughout the programming world, even serving as a consultant to the FBI. But there was another side to Max. As the black-hat 'Iceman', he'd seen the fraudsters around him squabble, their ranks riddled with infiltrators, their methods inefficient, and in their dysfunction was the ultimate challenge: he would stage a coup and steal their ill-gotten gains from right under their noses.Through the story of Max Butler's remarkable rise, KINGPIN lays bare the workings of a silent crime wave affecting millions worldwide. It exposes vast online-fraud supermarkets stocked with credit card numbers, counterfeit cheques, hacked bank accounts and fake passports. Thanks to Kevin Poulsen's remarkable access to both cops and criminals, we step inside the quiet,desperate battle that law enforcement fights against these scammers. And learn that the boy next door may not be all he seems.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    The manner in which computers are now able to mimic human thinking is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the input of data they are able to form their own decisions and take actions virtually as a human would. But as a machine, can consider many more scenarios and execute calculations to solve complex problems. This is the element that excites companies and budding machine learning engineers the most. The ability to solve complex problems never before attempted. This is also perhaps one reason why you are looking at purchasing this book, to gain a beginner's introduction to machine learning. This book provides a plain English introduction to the following topics: - Artificial Intelligence - Big Data - Downloading Free Datasets - Regression - Support Vector Machine Algorithms - Deep Learning/Neural Networks - Data Reduction - Clustering - Association Analysis - Decision Trees - Recommenders - Machine Learning Careers This book has recently been updated following feedback from readers. Version II now includes: - New Chapter: Decision Trees - Cleanup of minor errors

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    

    Anyone with a modest knowledge of HTML and web site management can learn to create dynamic content through the PHP programming language and the MySQL database. This book gives you the background and tools to do the job safely and reliably.Web Database Applications with PHP and MySQL, Second Edition thoroughly reflects the needs of real-world applications. It goes into detail on such practical issues as validating input (do you know what a proper credit card number looks like?), logging in users, and using templates to give your dynamic web pages a standard look.But this book goes even further. It shows how JavaScript and PHP can be used in tandem to make a user's experience faster and more pleasant. It shows the correct way to handle errors in user input so that a site looks professional. It introduces the vast collection of powerful tools available in the PEAR repository and shows how to use some of the most popular tools.Even while it serves as an introduction to new programmers, the book does not omit critical tasks that web sites require. For instance, every site that allows updates must handle the possibility of multiple users accessing data at the same time. This book explains how to solve the problem in detail with locking.Through a sophisticated sample application--Hugh and Dave's Wine Store--all the important techniques of dynamic content are introduced. Good design is emphasized, such as dividing logic from presentation. The book introduces PHP 5 and MySQL 4.1 features, while providing techniques that can be used on older versions of the software that are still in widespread use.This new edition has been redesigned around the rich offerings of PEAR. Several of these, including the Template package and the database-independent query API, are fully integrated into examples and thoroughly described in the text. Topics include:Installation and configuration of Apache, MySQL, and PHP on Unix®, Windows®, and Mac OS® X systems Introductions to PHP, SQL, and MySQL administration Session management, including the use of a custom database for improved efficiency User input validation, security, and authentication The PEAR repository, plus details on the use of PEAR DB and Template classes Production of PDF reports

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    It is a ‘ must’ book for those in every branch of science . . . in addition, economists, politicians, statesmen, and businessmen cannot afford to overlook cybernetics and its tremendous, even terrifying implications. "It is a beautifully written book, lucid, direct, and despite its complexity, as readable by the layman as the trained scientist." -- John B. Thurston, "The Saturday Review of Literature" Acclaimed one of the "seminal books . . . comparable in ultimate importance to . . . Galileo or Malthus or Rousseau or Mill," "Cybernetics" was judged by twenty-seven historians, economists, educators, and philosophers to be one of those books published during the "past four decades", which may have a substantial impact on public thought and action in the years ahead." -- Saturday Review

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    This second edition is appropriate as a supplementary (companion) text for any college-level course covering digital signal processing.

    Its flexible organization (with all chapters complete and self-contained) allows instructors the freedom to cover the topics they want in the order they choose.

    Led by three renowned internals experts, this classic guide is fully updated for Windows 7 and Windows Server 2008 R2—and now presents its coverage in two volumes.As always, you get critical insider perspectives on how Windows operates. And through hands-on experiments, you’ll experience its internal behavior firsthand—knowledge you can apply to improve application design, debugging, system performance, and support.In Part 1, you will:Understand how core system and management mechanisms work—including the object manager, synchronization, Wow64, Hyper-V, and the registryExamine the data structures and activities behind processes, threads, and jobsGo inside the Windows security model to see how it manages access, auditing, and authorizationExplore the Windows networking stack from top to bottom—including APIs, BranchCache, protocol and NDIS drivers, and layered servicesDig into internals hands-on using the kernel debugger, performance monitor, and other tools