A who's who of pathology experts delivers the most dependable, current, and complete coverage of today's essential pathology knowledge. At the same time, masterful editing and a practical organization make mastering every concept remarkably easy. Online access via Student Consult includes self-assessment and review questions, interactive case studies, downloadable images, videos, and a virtual microscope that lets you view slides at different magnifications. The result remains the ideal source for an optimal understanding of pathology. Offers the most authoritative and comprehensive, yet readable coverage available in any pathology textbook, making it ideal for USMLE or specialty board preparation as well as for course work. Includes access to the complete contents online via Student Consult, along with self-assessment and review questions, over 100 interactive clinical case studies, videos, and a virtual microscope that lets users view slides at different magnifications.Delivers a state-of-the-art understanding of the pathologic basis of disease through completely updated coverage, including the latest cellular and molecular biology.Demonstrates every concept visually with over 1,600 full-color photomicrographs and conceptual diagrams - many revised for even better quality.Facilitates learning with an outstanding full-color, highly user-friendly design.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    Long recognized as very applied and practical, Eggen and Kauchak's Educational Psychology: Windows on Classrooms, seventh edition is now even more applied and concise, giving students exactly what they need to know in the course. The author's hallmark cases remain, in both written and videotape format, to introduce real-world applications in a way that no other text can. Along with expanded applications to diversity (urban, suburban, and rural areas), technology, and a new pedagogical system that completely restructures how information is delivered in the book and will help students really understand what they should be getting out of every single chapter. The text now comes with two new DVDs of video material and an access code for the new Teacher Prep Website that will be automatically shrinkwrapped with all new copies of the text. Educational Psychology: Windows on Classrooms once again truly fulfills the promise of its title, giving students a window on the classrooms in which they will someday teach.

    This updated edition of Ross's classic bestseller provides an introduction to elementary probability theory and stochastic processes, and shows how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries.This book now contains a new section on compound random variables that can be used to establish a recursive formula for computing probability mass functions for a variety of common compounding distributions; a new section on hiddden Markov chains, including the forward and backward approaches for computing the joint probability mass function of the signals, as well as the Viterbi algorithm for determining the most likely sequence of states; and a simplified approach for analyzing nonhomogeneous Poisson processes. There are also additional results on queues relating to the conditional distribution of the number found by an M/M/1 arrival who spends a time t in the system; inspection paradox for M/M/1 queues; and M/G/1 queue with server breakdown. Furthermore, the book includes new examples and exercises, along with compulsory material for new Exam 3 of the Society of Actuaries.This book is essential reading for professionals and students in actuarial science, engineering, operations research, and other fields in applied probability.

    The book's holistic perspective shows students how nursing care involves more than performing procedures. With a case-based approach to learning, the book offers numerous examples and opportunities for students to think critically. The Seventh Edition includes new features highlighting clinical questions and research, nursing advocacy, and health literacy, and also includes new information related to electronic medical records and documentation. With this textbook you'll find an integrated, cohesive, and student-focused suite of multimedia products to appeal to all types of learners, including a bound-in DVD-ROM and a companion website.

    It offers superior coverage of all key areas, including descriptive chemistry, MO theory, bonding, and physical inorganic chemistry. Chapter topics are presented in logical order and include: basic concepts; nuclear properties; an introduction to molecular symmetry; bonding in polyatomic molecules; structures and energetics of metallic and ionic solids; acids, bases, and ions in aqueous solution; reduction and oxidation; non-aqueous media; and hydrogen. Four special topic chapters, chosen for their currency and interest, conclude the book. For researchers seeking the latest information in the field of inorganic chemistry.

    

     Chess is one of the world’s most popular board games, and is played by millions of people from all walks of life in parks, homes, online and tournaments. This two-player game is played on a checkered board with 64 squares arranged in an eight by eight grid called a chessboard. The game begins with each player possessing 16 pieces, 8 pawns, two rooks (sometimes called castles), two bishops, two knights, a queen and a king. The different player’s pieces are colored differently and are usually either black or white, though colors may vary. Regardless of the color variation though, each ‘team’ is referred to as either black or white. The objective of the game is to ‘checkmate’ the opponent’s king by placing it in such a position that it cannot escape capture. There are other methods to winning the game, such as when an opponent forfeits or resigns from the match. Forfeitures are normally as a result of the opponent losing too many pieces, or if a checkmate is seen as inevitable. There are also circumstances where the game ends in a draw for various reasons, meaning that neither player wins. The first official world chess championship was held in 1886 between Wilhelm Steinitz and Johannes Zukerfort. The victor and thus the first official World Champion was Wilhelm Seinitz. Since the creation of the World Chess Federation in Paris in 1924, the best chess players in the world have been awarded the title of Grandmaster, which is the highest accolade a chess player can get apart from world champion. The latter half of the 20th century saw computers being programmed to play chess, with many home computers now being able to play with such a high level of sophistication that they can outwit some of the best human players. The first computer to ever beat a reigning world champion was the computer Deep Blue, which beat Garry Kasparov in a match in 1997. What You're Going to Learn: Origins and History of Chess Rules and Notations Movements in the Game The Pieces, Their Movements and Values Chess Strategy and Tactics Tips for Beginners What Not to Do More Tactics ...And Much More! Ready to Play Like a Pro ? * * * DOWNLOAD YOUR COPY TODAY * * *

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. This book can be used for readers who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    The book covers four major topics in field experimentation:

    It provides the general knowledge of mammalian structure that is crucial to the understanding of the other basic sciences, as well as the more detailed information that is directly applicable to the practice of veterinary medicine. Discussing the various body systems, it then offers a working knowledge of them by addressing the regional anatomy of a particular species or group of species.Content is divided into two main sections - body systems and specific species or group of species - for a thorough and more logical study of anatomy.All chapters feature a consistent format, offering a simple and even flow of information.Color inserts enhance the overall quality of the book and better illustrate various anatomical areas of concern.Both small and large animals are covered - including the horse, pig, cow, dog, and cat - to emphasize features and topics that have direct relevance to clinical work. Also included is a chapter on Avian Anatomy.Content has been streamlined to include only the most important information.A new section on imaging greatly enhances diagnostic procedures.The addition of new illustrations provides an emphasis on surface anatomy throughout the text.

    There are equally many advanced textbooks which delve into the far reaches of statistical theory, while bypassing practical applications. But between these two approaches is an unfilled gap, in which theory and practice merge at an intermediate level. Professor M. G. Bulmer's Principles of Statistics, originally published in 1965, was created to fill that need. The new, corrected Dover edition of Principles of Statistics makes this invaluable mid-level text available once again for the classroom or for self-study.Principles of Statistics was created primarily for the student of natural sciences, the social scientist, the undergraduate mathematics student, or anyone familiar with the basics of mathematical language. It assumes no previous knowledge of statistics or probability; nor is extensive mathematical knowledge necessary beyond a familiarity with the fundamentals of differential and integral calculus. (The calculus is used primarily for ease of notation; skill in the techniques of integration is not necessary in order to understand the text.)Professor Bulmer devotes the first chapters to a concise, admirably clear description of basic terminology and fundamental statistical theory: abstract concepts of probability and their applications in dice games, Mendelian heredity, etc.; definitions and examples of discrete and continuous random variables; multivariate distributions and the descriptive tools used to delineate them; expected values; etc. The book then moves quickly to more advanced levels, as Professor Bulmer describes important distributions (binomial, Poisson, exponential, normal, etc.), tests of significance, statistical inference, point estimation, regression, and correlation. Dozens of exercises and problems appear at the end of various chapters, with answers provided at the back of the book. Also included are a number of statistical tables and selected references.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.