With over 1,000 pages and more than 20 chapters, it's packed with the most up-to-date information students need to get ready for the test.Students will gain valuable experience and raise their confidence by taking practice tests, by reviewing critical concepts, test-taking approaches, and focused sets of practice questions just like those on the actual SAT.The Official SAT Study Guide: Second Edition will help students get ready for the SAT with:- 10 official SAT practice tests, including 3 new recent exams- detailed descriptions of math, critical reading, and writing sections of the SAT- targeted practice questions for each SAT question type- practice essay questions, along with sample essays and annotations- a review of math concepts tested in the exam- test-taking approaches and suggestions that underscore important points- free online score reports- exclusive access to online answers and explanations at collegeboard.org- $10 discount on The Official SAT Online Course to all book ownersThere's also a complete chapter on the PSAT/NMSQT®.

    Noted for its crystal clear presentation and easy-to-follow problem solving methodology, Incropera and Dewitt's systematic approach to the first law develops reader confidence in using this essential tool for thermal analysis. Readers will learn the meaning of the terminology and physical principles of heat transfer as well as how to use requisite inputs for computing heat transfer rates and/or material temperatures.

    Includes the most comprehensive entries on major philosophers, 400 new entries including over 50 on preeminent contemporary philosophers, extensive coverage of rapidly developing fields such as the philosophy of mind and applied ethics, more entries on non-Western philosophy than any comparable volume, and increased coverage of Continental philosophy.

    This exciting new book, from a leading expert in working with expectant dads, doesn’t sideline or speak down to men. Instead it provides an array of targeted information to fully prepare men for their new roles – as both birth partners and fathers.The Expectant Dad’s Handbook is a one-stop guide for men on their path to fatherhood. It provides practical answers to all the questions on the mind of a dad-to-be – from what to expect at each stage of pregnancy to how to cope with any worries and fears about becoming a dad. It also reveals unique insights into a dad’s role during labour, showing key strategies for improving the birth.Both practical and accessible, this guide will provide all the information and advice fathers need for the journey ahead.

    The biological background provided enables students to comprehend the major problems in biochemical engineering and formulate effective solutions.

    Numerous tables and figures.

    Part I introduces concepts of thermodynamics and statistical mechanics from a unified view. Parts II and III explore further applications of classical thermodynamics and statistical mechanics. Throughout, the emphasis is on real-world applications.

    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.

    Driver, and Charles Briggs--spent over twenty years researching, writing, and preparing "The Brown-Driver-Briggs Hebrew and English Lexicon." Since it first appeared in the early part of the twentieth century, BDB has been considered the finest and most comprehensive Hebrew lexicon available to the English-speaking student. Based upon the classic work of Wilhelm Gesenius, the "father of modern Hebrew lexicography," BDB gives not only dictionary definitions for each word, but relates each word to its Old Testament usage and categorizes its nuances of meaning. BDB's exhaustive coverage of Old Testament Hebrew words, as well as its unparalleled usage of cognate languages and the wealth of background sources consulted and quoted, render BDB and invaluable resource for all students of the Bible.

    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.

    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.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    New problems added throughout.

    Fully updated and now in two colour, the text is accessible and well structured with numerous real life examples and student learning aids. The text is also accompanied by a fully comprehensive companion web site.

    Former co-teacher and national presenter Anne Beninghof shares stories, and real-life co-taught lesson examples that emphasize creative yet time-efficient instructional strategies that lend themselves beautifully to the co-taught classroom. Teachers and instructional leaders at all levels and in a wide variety of content areas will find this book replete with valuable co-teaching guidance so that success is guaranteed.Offers tips for effective teaching strategies for every type of team teaching situation imaginable Includes guidelines for successful team-teaching with specialists in technology; literacy; occupational/physical therapy; special education; speech-language therapy; ELL; gifted The author is an internationally recognized consultant and trainer This user-friendly, comprehensive book is filled with concrete ideas teachers can implement immediately in the classroom to boost student learning and engagement.