It covers the entire scope of mechanical metallurgy, from an understanding of the continuum description of stress and strain, through crystalline and defect mechanisms of flow and fracture, and on to a consideration of major mechanical property tests and the basic metalworking process. It has been updated throughout, SI units have been added, and end-of-chapter study questions are included.

    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.

    Das' Sixth Edition of PRINCIPLES OF FOUNDATION ENGINEERING maintains the careful balance of current research and practical field applications that has made it the leading text in foundation engineering courses. Featuring a wealth of worked-out examples and figures that help students with theory and problem-solving skills, the book introduces civil engineering students to the fundamental concepts and application of foundation analysis design. Throughout, Das emphasizes the judgment needed to properly apply the theories and analysis to the evaluation of soils and foundation design as well as the need for field experience. The sixth edition contains many new homework and worked-out problems.

    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.

    The layout of the book covers the following: Laplace transforms, mathematical model

    These sections together make an appropriate one-term advanced/graduate level course (mathematical prerequisites: vector analysis and simple partial-differential equations). The book is printed to make it easy for readers to identify these sections.• The remaining Track 2 material provides a wealth of advanced topics instructors can draw from to flesh out a two-term course, with Track 1 sections serving as prerequisites.

    

    Written with student centred, pedagogically driven approach, the text provides a self-contained introduction to the theory of signals and systems. This book looks at the concepts of systems, and also examines signals and the way that signals interact with physical systems. It covers topics ranging from basic signals and systems to signal analysis, properties of continuous-time Fourier transforms including Fourier transforms of standard signals, signal transmission through linear systems, relation between convolution and correlation of signals, sampling theorems and techniques, and transform analysis of LTI systems. All the solved and unsolved problems in this book are designed to illustrate the topics in a clear way.

    Extraterrestrials ... Our genetic destiny ... The roots of language and learning ... Quantum physics and the shape of the universe ... Artificial intelligence .. In a masterful "trial by reason," author John L. Casti presents all sides of the most important and vital scientific debates raging in the world today -- scrutinizing six perplexing "great questions" in the most engaging, astonishing and accessible amalgam of science and literature since "A Brief History of Time.

    The text is divided into four parts: circuits, electronics, digital systems, and electromagnetics. Although it delves in depth into each of these topics, the text represents more than your basic survey of the basics of electrical engineering. A solid understanding of the fundamental principles on which modern electrical engineering is based is also provided. This edition includes a chapter on circuit analysis software SPICE, with a detailed discussion of the PC version known as PSPICE (from MicroSim Corp.). Numerous drill exercises have been added to this new edition, reinforcing ideas presented in the examples. There are over 1,000 end-of-chapter problems. This text is suitable for a variety of electrical engineering courses. It can be used as a text for an introduction to electrical engineering for both majors and non-majors or both, or can be split and the various chapters utilized for an introduction to circuits course, a first electronics course, or for a course on digital electronics and logic design.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    In this Very Short Introduction, one of the world's leading authorities on the philosophy of information and on information ethics, Luciano Floridi, offers an illuminating exploration of information as it relates to both philosophy and science. He discusses the roots of the concept of information in mathematics and science, and considers the role of information in several fields, including biology. Floridi also discusses concepts such as "Infoglut" (too much information to process) and the emergence of an information society, and he addresses the nature of information as a communication process and its place as a physical phenomenon. Perhaps more important, he explores information's meaning and value, and ends by considering the broader social and ethical issues relating to information, including problems surrounding accessibility, privacy, ownership, copyright, and open source. This book helps us understand the true meaning of the concept and how it can be used to understand our world.About the Series: Combining authority with wit, accessibility, and style, Very Short Introductions offer an introduction to some of life's most interesting topics. Written by experts for the newcomer, they demonstrate the finest contemporary thinking about the central problems and issues in hundreds of key topics, from philosophy to Freud, quantum theory to Islam.

    This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis. Organized into four distinct sections, Statistics in a Nutshell offers you:Introductory material: Different ways to think about statistics Basic concepts of measurement and probability theoryData management for statistical analysis Research design and experimental design How to critique statistics presented by others Basic inferential statistics: Basic concepts of inferential statistics The concept of correlation, when it is and is not an appropriate measure of association Dichotomous and categorical data The distinction between parametric and nonparametric statistics Advanced inferential techniques: The General Linear Model Analysis of Variance (ANOVA) and MANOVA Multiple linear regression Specialized techniques: Business and quality improvement statistics Medical and public health statistics Educational and psychological statistics Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests. With Statistics in a Nutshell, you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.

    

    This revised handbook on chemical engineering includes new topics such as biochemical engineering, waste management, plant safety and analysis of plant performance, handling of hazardous materials, computer applications, expert systems, and material and energy balances.