Following an introduction to neural structure and function, all the key methods and procedures of cognitive neuroscience are explained, with a view to helping students understand how they can be used to shed light on the neural basis of cognition.The second part of the book goes on to present an up-to-date overview of the latest theories and findings in all the key topics in cognitive neuroscience, including vision, attention, memory, speech and language, numeracy, executive function and social and emotional behaviour. Throughout, case studies, newspaper reports and everyday examples are used to provide an easy way in to understanding the more challenging ideas that underpin the subject.In addition each chapter includes:Summaries of key terms and points Example essay questions to aid exam preparation Recommended further reading Feature boxes exploring interesting and popular questions and their implications for the subject.Written in an engaging style by a leading researcher in the field, this book will be invaluable as a core text for undergraduate modules in cognitive neuroscience. It can also be used as a key text on courses in cognition, cognitive neuropsychology or brain and behaviour. Those embarking on research will find it an invaluable starting point and reference.We offer CD-ROM-based resources free of charge to instructors who recommend The Student's Guide to Cognitive Neuroscience by Jamie Ward. These resources include:A chapter-by-chapter, illustrated slideshow lecture course An innovative bank of multiple-choice questions, graded according to difficulty and which allow for confidence-weighted answers Comprehensive lecture planning advice tailored to different length courses.Jamie Ward has researched and taught extensively in many areas of cognitive neuroscience. He is a leading authority on the subject of synaesthesia and has contributed to a wider understanding of it in both academic and lay circles.

    The title says it is the hard way to learn to writecode but it’s actually not. It’s the “hard” way only in that it’s the way people used to teach things. In this book youwill do something incredibly simple that all programmers actually do to learn a language: 1. Go through each exercise. 2. Type in each sample exactly. 3. Make it run.That’s it. This will be very difficult at first, but stick with it. If you go through this book, and do each exercise for1-2 hours a night, then you’ll have a good foundation for moving on to another book. You might not really learn“programming” from this book, but you will learn the foundation skills you need to start learning the language.This book’s job is to teach you the three most basic essential skills that a beginning programmer needs to know:Reading And Writing, Attention To Detail, Spotting Differences.

    All the tips, techniques, strategies and advice are focused on maximizing students’ score by increasing their task-solving speed and efficiency, and preventing typical mistakes.

    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.

    He has, as they say, been around a bit. And as a result, he's got one or two things to tell us about how it all works; and being Jeremy Clarkson he's not about to voice them quietly, humbly and without great dollops of humour.In The World According to Clarkson, he reveals why it is that:Too much science is bad for our health'70s rock music is nothing to be ashamed ofHunting foxes while drunk and wearing night-sights is neither big nor cleverWe must work harder to get rid of cricketHe likes the Germans (well, sometimes)With a strong dose of common sense that is rarely, if ever, found inside the M25, Clarkson hilariously attacks the pompous, the ridiculous, the absurd and the downright idiotic, whilst also celebrating the eccentric, the clever and the sheer bloody brilliant.Less a manifesto for living and more a road map to modern life, The World According to Clarkson is the funniest book you'll read this year. Don't leave home without it.

    Features exceptionally clear explanations and descriptions, step-by-step examples, more than 50 practical applications, over 2000 easy-to-challenging practice problems, and comprehensive coverage of essentials. PSpice, OrCAd version 9.2 Lite Edition, Multisims 2001 version of Electronics Workbench, and MathCad software references and examples are used throughout. Computer programs (C++, BASIC and PSpice) are printed in color, as they run, at the point in the book where they are discussed. Current and Voltage. Resistance. Ohm's Law, Power, and Energy. Series Circuits. Parallel Circuits. Series-Parallel Networks. Methods of Analysis & Selected Topics. Network Theorems. Capacitors. Magnetic Circuits. Inductors. Sinusodial Alternating Waveforms. The Basic Elements and Phasors. Series and Parallel ac Circuits. Series-Parallel ac Networks. Methods of Analysis and Related Topics. Network Theorems (ac). Power (ac). Resonance. Transformers. Polyphase Systems. Decibels, Filters, and Bode Points. Pulse Waveforms and the R-C Response. Nonsinusodial Circuits. System Analysis: An Introduction. For those working in electronic technology.

    This ninth edition covers both analytical and empirical approaches to the subject. The examples and templates provide students with resources for computer-numerical solutions.

    

    It is the definitive reference guide, now in a second edition. Although the first edition was written in 1978, it continues to be a worldwide best-seller. This second edition brings the classic original up to date to include the ANSI standard. From the Preface: We have tried to retain the brevity of the first edition. C is not a big language, and it is not well served by a big book. We have improved the exposition of critical features, such as pointers, that are central to C programming. We have refined the original examples, and have added new examples in several chapters. For instance, the treatment of complicated declarations is augmented by programs that convert declarations into words and vice versa. As before, all examples have been tested directly from the text, which is in machine-readable form. As we said in the first preface to the first edition, C "wears well as one's experience with it grows." With a decade more experience, we still feel that way. We hope that this book will help you to learn C and use it well.

    This friendly, easy-to-read guide gently introduces you to the most commonly used features of SAS software plus a whole lot more! Authors Lora Delwiche and Susan Slaughter have revised the text to include concepts of the Output Delivery System; the STYLE= option in the PRINT, REPORT, and TABULATE procedures; ODS HTML, RTF, PRINTER, and OUTPUT destinations; PROC REPORT; more on PROC TABULATE; exporting data; and the colon modifier for informats. You'll find clear and concise explanations of basic SAS concepts (such as DATA and PROC steps), inputting data, modifying and combining data sets, summarizing and presenting data, basic statistical procedures, and debugging SAS programs. Each topic is presented in a self-contained, two-page layout complete with examples and graphics. This format enables new users to get up and running quickly, while the examples allow you to type in the program and see it work!

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    Lang, one of the worlds foremost origami artists and scientists, presents the never-before-described mathematical and geometric principles that allow anyone to design original origami, something once restricted to an elite few. From the theoretical underpinnings to detailed step-by-step folding sequences, this book takes a modern look at the centuries-old art of origami.