It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    This edition provides increased coverage of taste and smell, circadian rhythms, brain development, and developmental disorders and includes new information on molecular mechanisms and functional brain imaging. Path of Discovery boxes, written by leading researchers, highlight major current discoveries. In addition, readers will be able to assess their knowledge of neuroanatomy with the Illustrated Guide to Human Neuroanatomy, which includes a perforated self-testing workbook.This edition's robust ancillary package includes a bound-in student CD-ROM, an Instructor's Resource CD-ROM, and resources online.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information

    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.

    Now Schaum's is better than ever-with a new look, a new format with hundreds of practice problems, and completely updated information to conform to the latest developments in every field of study.Fully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores!Schaum's Outlines-Problem Solved.

    Intermediate to advanced system administrators will find more than 100 tried-and-tested scripts they can copy and use immediately.Updated for PowerShell 3.0, this comprehensive cookbook includes hands-on recipes for common tasks and administrative jobs that you can apply whether you’re on the client or server version of Windows. You also get quick references to technologies used in conjunction with PowerShell, including format specifiers and frequently referenced registry keys to selected .NET, COM, and WMI classes.Learn how to use PowerShell on Windows 8 and Windows Server 2012Tour PowerShell’s core features, including the command model, object-based pipeline, and ubiquitous scriptingMaster fundamentals such as the interactive shell, pipeline, and object conceptsPerform common tasks that involve working with files, Internet-connected scripts, user interaction, and moreSolve tasks in systems and enterprise management, such as working with Active Directory and the filesystem

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

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    Whether you're a budding hobbyist or an engineer, you'll benefit from the perfectly paced lessons that walk you through useful, artistic, and educational exercises that gradually get more advanced. In addition to specific projects, the book shares best practices in programming and design that you can apply to your own projects. Code snippets and schematics will serve as a useful reference for future projects even after you've mastered all the topics in the book.Includes a number of projects that utilize different capabilities of the Arduino, while interfacing with external hardware Features chapters that build upon each other, tying in concepts from previous chapters to illustrate new ones Includes aspects that are accompanied by video tutorials and other multimedia content Covers electrical engineering and programming concepts, interfacing with the world through analog and digital sensors, communicating with a computer and other devices, and internet connectivity Explains how to combine smaller topics into more complex projects Shares downloadable materials and source code for everything covered in the book Projects compatible with many official Arduino boards including Arduino Uno; Arduino Leonardo; Arduino Mega 2560; Arduino Due; Arduino Nano; Arduino Mega ADK; LilyPad Arduino and may work with Arduino-compatible boards such as Freeduino and new third party certified boards such as the Intel Galileo Exploring Arduino takes you on an adventure and provides you with exclusive access to materials not found anywhere else!

    Try out more than 1,000 commands to find and get software, monitor system health and security, and access network resources. Then, apply the skills you learn from this book to use and administer desktops and servers running Ubuntu, Debian, and KNOPPIX or any other Linux distribution.

    Designed to prepare you for the general FE exam in the least amount of time, this review manual provides you with a complete and comprehensive review of the topics covered on the FE exam. Diagnostic exams on 13 separate topics help you identify where you need the most review, and the chapters that follow each exam provide the information you need to get up to speed in those areas. Over 1,200 practice problems give you experience in solving exam-like problems, while you can use the realistic 8-hour practice exam to simulate the actual FE exam.Everything You Need to Succeed on the FE/EIT ExamOver 1,200 practice problems, with step-by-step solutions13 diagnostic exams help you to assess your strengths and weaknessesAn 8-hour practice exam, with 180 multiple-choice questionsSI units throughout, just like the exam50 short chapters create manageable study blocksNCEES nomenclature and formulasSample study scheduleExam tips and advice from recent examinees

    In eight chapters, it provides the most thorough coverage of TCP available. It also covers the newest TCP/IP features, including multicasting, path MTU discovery and long fat pipes. The author describes various protocols, including ARP, ICMP and UDP. He utilizes network diagnostic tools to actually show the protocols in action. He also explains how to avoid silly window syndrome (SWS) by using numerous helpful diagrams. This book gives you a broader understanding of concepts like connection establishment, timeout, retransmission and fragmentation. It is ideal for anyone wanting to gain a greater understanding of how the TCP/IP protocols work.

    The eighth edition has been revised to include updated coverage of NMR Spectroscopy and UV spectroscopy. New questions at the end of chapters reinforce the skills and techniques learned. Emphasis is placed on green chemistry in the lab, focusing on the more environmentally friendly materials that can be used. In addition, updated discussions are included on safety, distillation, gas chromatography, and liquid chromatography. This gives organic chemists the most up-to-date information to enhance their lab skills.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.