Recommendations based on "Classical Mechanics"

The Table of Integrals, Series, and Products is the essential reference for integrals in the English language. Mathematicians, scientists, and engineers, rely on it when identifying and subsequently solving extremely complex problems. Since publication of the first English-language edition in 1965, it has been thoroughly revised and enlarged on a regular basis, with substantial additions and, where necessary, existing entries corrected or revised. The seventh edition includes a fully searchable CD-Rom.- Fully searchable CD that puts information at your fingertips included with text- Most up to date listing of integrals, series andproducts - Provides accuracy and efficiency in work

"Wald's book is clearly the first textbook on general relativity with a totally modern point of view; and it succeeds very well where others are only partially successful. The book includes full discussions of many problems of current interest which are not treated in any extant book, and all these matters are considered with perception and understanding."—S. Chandrasekhar"A tour de force: lucid, straightforward, mathematically rigorous, exacting in the analysis of the theory in its physical aspect."—L. P. Hughston, Times Higher Education Supplement "Truly excellent.... A sophisticated text of manageable size that will probably be read by every student of relativity, astrophysics, and field theory for years to come."—James W. York, Physics Today

Plant Systematics contains the essential paradigms, concepts, and terms required for a basic understanding of plant systematics at the graduate or undergraduate level. Plant systematics is an area central to numerous other biological disciplines, and a large subset of plant scientists are required to take a course in plant systematics. Almost all ecologists, horticulturalists, plant developmental biologists, and plant pathologists are interested in plant systematics because it is central to their studies of the plants that form such a large part of every ecosystem and experimental system.* The Henry Allan Gleason Award of The New York Botanical Garden, awarded for "Outstanding recent publication in the field of plant taxonomy, plant ecology, or plant geography" (2006)* Contains numerous cladograms that illustrate the evolutionary relationships of major plant groups, with an emphasis on the adaptive significance of major evolutionary novelties *Provides descriptions and classifications of major groups of angiosperms, including over 90 flowering plant families * Includes a comprehensive glossary of plant morphological terms as well as appendices on botanical illustration and plant description

Learn How Coupling Impacts Every Software Design Decision You Make--and How to Control It If you want to build modular, evolvable, and resilient software systems, you have to get coupling right. Every design decision you make influences coupling, which in turn shapes the design options available to you. Despite its importance, coupling often doesn't receive the attention it deserves--until now. Since the dawn of software engineering, it's been clear that proper management of coupling is essential for architecting modular software systems. This topic has been extensively researched over the years, but some of that knowledge has been forgotten, and some of it is challenging to apply in this day and age. In Balancing Coupling in Software Design, author Vlad Khononov has built a model that not only draws on this accumulated knowledge, but also adapts it to modern software engineering practices, offering a fresh perspective on modular software design. With principles grounded in practice, Vlad teaches you not only to navigate the multi-dimensional nature of coupling but also to use coupling as a tool for managing complexity and increasing modularity. And who knows, maybe this book will change the way you think about software design as whole. Defines the concept of coupling and the role it plays in system design and architecture Explains how coupling can both increase complexity and foster modularity of a system Introduces a holistic model that turns coupling into a tool for designing modular software Shows how to evolve design decisions to support continuous growth of software systems Illustrates the discussed principles with detailed examples based on real-life case studies "Coupling is one of those words that is used a lot, but little understood. Vlad propels us from simplistic slogans like 'always decouple components' to a nuanced discussion of coupling in the context of complexity and software evolution. If you build modern software, read this book!" --Gregor Hohpe, author of The Software Architect Elevator Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Global Physical Climatology is an introductory text devoted to the fundamental physical principles and problems of climate sensitivity and change. Addressing some of the most critical issues in climatology, this text features incisive coverage of topics that are central to understanding orbital parameter theory for past climate changes, and for anthropogenic and natural causes of near-future changes--Key Features* Covers the physics of climate change* Examines the nature of the current climate and its previous changes* Explores the sensitivity of climate and the mechanisms by which humans are likely to produce near-future climate changes* Provides instructive end-of-chapter exercises and appendices

Praise for the First Edition:"I recommend this book, without hesitation, as either a reference or course text...Wilks' excellent book provides a thorough base in applied statistical methods for atmospheric sciences."--BAMS (Bulletin of the American Meteorological Society)Fundamentally, statistics is concerned with managing data and making inferences and forecasts in the face of uncertainty. It should not be surprising, therefore, that statistical methods have a key role to play in the atmospheric sciences. It is the uncertainty in atmospheric behavior that continues to move research forward and drive innovations in atmospheric modeling and prediction.This revised and expanded text explains the latest statistical methods that are being used to describe, analyze, test and forecast atmospheric data. It features numerous worked examples, illustrations, equations, and exercises with separate solutions. Statistical Methods in the Atmospheric Sciences, Second Edition will help advanced students and professionals understand and communicate what their data sets have to say, and make sense of the scientific literature in meteorology, climatology, and related disciplines. Accessible presentation and explanation of techniques for atmospheric data summarization, analysis, testing and forecasting Many worked examples End-of-chapter exercises, with answers provided

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason—to reach conclusions based on available information. 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.

Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tübingen. Schölkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by The MIT Press.

According to Webster's Dictionary, a fascicle is "one of the division of a bookpublished in parts."This material represents significant updates to Volume 1, Third Edition ofDonald Knuth's The Art of Computer Programming.Knuth's fascicle philosophy is as follows: "The material will first appear in betatestform as fascicles of approximately 128 pages each, issued approximatelytwice per year. These fascicles will represent my best attempt to write acomprehensive account, but computer science has grown to the point where Icannot hope to be an authority on all the material covered in these books.Therefore I'll need feedback from readers in order to prepare the officialvolumes later."