This text helps develop in readers the skills, knowledge and strategies needed to read and interpret research reports and to evaluate the quality of such reports.

    This authoritative book scrapes away these bad features to reveal a subset of JavaScript that's more reliable, readable, and maintainable than the language as a whole--a subset you can use to create truly extensible and efficient code.Considered the JavaScript expert by many people in the development community, author Douglas Crockford identifies the abundance of good ideas that make JavaScript an outstanding object-oriented programming language-ideas such as functions, loose typing, dynamic objects, and an expressive object literal notation. Unfortunately, these good ideas are mixed in with bad and downright awful ideas, like a programming model based on global variables.When Java applets failed, JavaScript became the language of the Web by default, making its popularity almost completely independent of its qualities as a programming language. In JavaScript: The Good Parts, Crockford finally digs through the steaming pile of good intentions and blunders to give you a detailed look at all the genuinely elegant parts of JavaScript, including:SyntaxObjectsFunctionsInheritanceArraysRegular expressionsMethodsStyleBeautiful featuresThe real beauty? As you move ahead with the subset of JavaScript that this book presents, you'll also sidestep the need to unlearn all the bad parts. Of course, if you want to find out more about the bad parts and how to use them badly, simply consult any other JavaScript book.With JavaScript: The Good Parts, you'll discover a beautiful, elegant, lightweight and highly expressive language that lets you create effective code, whether you're managing object libraries or just trying to get Ajax to run fast. If you develop sites or applications for the Web, this book is an absolute must.

    What's new in the Second Edition? 1. Fully compliant with the latest version of SPSS version 12 2. More coverage of advanced statistics including completely new coverage of non-parametric statistics. The book is 50 per cent longer than the First Edition. 3. Each section of each chapter now has a notation - 1,2 or 3 - referring to the intended level of study. This helps students navigate their way through the book and makes it user-friendly for students of ALL levels. 4. Has a 'how to use this book' section at the start of the text. 5. Characters in each chapter have defined roles - summarizing key points, to pose questions etc 6. Each chapter now has several examples for students to work through. Answers provided on the enclosed CD-ROM

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality. Examples from classical statistics are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning.

    In the highly litigated area of Special Education, it is imperative that professionals in the field understand the legal requirements of providing a free appropriate public education to students with disabilities. This text presents the necessary information for educators to understand the history and development of special education laws and the requirements of these laws. It provides the reader with the necessary skills to locate pertinent information in law libraries, on the Internet, and other sources to keep abreast of the constant changes and developments in the field. The second edition of The Law and Special Education, one of the top special education law books in the field, includes new information on the Individuals with Disabilities Education Act of 2004 and the No Child Left Behind Act of 2001. It has been updated with the latest information on the statutes, regulations, policy guidance, and cases on special education law.

    Used in hundreds of educational institutions around the world as the primary text for CNC courses, and used daily by many in-field CNC programmers and machine operators, this book literally defines CNC programming. Written with careful attention to detail, there are no compromises. Many of the changes in this new Third Edition are the direct result of comments and suggestions received from many CNC professionals in the field. This extraordinarily comprehensive work continues to be packed with over one thousand illustrations, tables, formulas, tips, shortcuts, and practical examples.The enclosed CD-ROM now contains a fully functional 15-day shareware version of CNC tool path editor/simulator, NCPlot™. This powerful, easy-to-learn software includes an amazing array of features, many not found in competitive products. NCPlot offers an unmatched combination of simplicity of use and richness of features. Support for many advanced control options is standard, including a macro interpreter that simulates Fanuc and similar macro programs.The CD-ROM also offers many training exercises based on individual chapters, along with solutions and detailed explanations. Special programming and machining examples are provided as well, in form of complete machine files, useful as actual programming resources. Virtually all files use Adobe PDF format and are set to high resolution printing.FEATURES Fully functional shareware version of CNC toolpath simulator/editor, NCPlot(TM), included on the CD-ROM. This powerful software includes an amazing array of features, including those not found in competitive products. Support for many advanced features is standard, and the included macro interpreter can simulate Fanuc and compatible macro toolpath programs Detailed section on CNC lathes with live tooling, including examples Image files of many actual parts, used as examples More programming examples (both in printed text and on the CD-ROM) Optimized for the latest Fanuc and related control systems Additional formulas, calculations and handy reference material Fourth axis programming (indexing and rotary) CD-ROM based projects, including several as interactive PDF forms Improved index for better search of topics

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

    It provides dozens of new examples from actual research studies are used to provide illustrations of concepts and methods. Key terms are now called out and defined in boxes at the bottom of the pages where they appear, for easier study and review. In chapter 1, there are now separate descriptions and examples of the steps in the research process for quantitative and qualitative approaches, to underscore some of the fundamental differences. Chapter 2 has new discussions of participatory action research, instrumental and reflexive knowledge, the various audiences for social research findings, and researcher autonomy when research is commissioned. The discussion of social theories in Chapter 3 now covers levels of abstraction, and relationships among concepts

    I've examined every one and Cipani's is clearly the best. The assessment part is great, and I particularly like the protocol format for interventions. Cipani's text nicely fills a big gap between research and application. Were I teaching a seminar to clinicians, I think the text would be perfect. -- Brian A. Iwata, PhD, University of FloridaProfessionals who work in mental health and educational settings are frequently faced with clients (children, adolescents, adults) who engage in serious problem behaviors. Such behaviors often impact the client's welfare and ability to live, work, and be educated in mainstream environments. Children and adolescents who manifest these behaviors are particularly vulnerable to these disruptions, which can have a far-reaching impact on their development and future prospects.This practical book, written both for clinician/educators and high-level students, creates a function-based behavioral diagnostic classification system, the first of its kind, as well as treatment protocols that fit such a diagnostic system. Heavily practitioner-oriented, the book will address the full range of behaviors - ranging from aggression, self-injury, stereotypic behavior (repetitive body movements), tantrums, and non-compliance - with real life and hypothetical cases to help clinicians think through the full range of treatment options. Unique in moving beyond functional assessment to assessment diagnosis and treatment, this book will be highly useful for mental health clinicians, students of Advanced Behavior Analysis, and special education practitioners among others.Professor Cipani has also prepared extensive ancillary material for use in teaching this book and will make it available to anyone who has adopted it for course use. Instructors who have adopted the title may inquire of Professor Cipani at ennioc26@hotmail.com

    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.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and PygalMake 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progressesWork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!

    It can be freely downloaded and it works on multiple computer platforms. This book provides an elementary introduction to R. In each chapter, brief introductory sections are followed by code examples and comments from the computational and statistical viewpoint. A supplementary R package containing the datasets can be downloaded from the web.