Generous support from a number of foundations provided the means for assembling and maintaining an experienced staff to co-operate with members of the Institute's Physics Department in the examination, improvement, and development of physics curriculum materials for students planning careers in the sciences. After careful analysis of objectives and the problems involved, preliminary versions of textbooks were prepared, tested through classroom use at M.I.T. and other institutions, re-evaluated, rewritten, and tried again. Only then were the final manuscripts undertaken.

    Johnson. It was the first book exclusively on the theory of NP-completeness and computational intractability. The book features an appendix providing a thorough compendium of NP-complete problems (which was updated in later printings of the book). The book is now outdated in some respects as it does not cover more recent development such as the PCP theorem. It is nevertheless still in print and is regarded as a classic: in a 2006 study, the CiteSeer search engine listed the book as the most cited reference in computer science literature.

    

    DLC: Quantum theory.

    New to this third edition are expanded coverage of such important topics as surface boundary interfaces, improved discussions of such physical and mathematical laws as the Law of Biot and Savart and the Euler Momentum Integral. A very important new section on Computational Fluid Dynamics has been added for the very first time to this edition. Expanded and improved end-of-chapter problems will facilitate the teaching experience for students and instrutors alike. This book remains one of the most comprehensive and useful texts on fluid mechanics available today, with applications going from engineering to geophysics, and beyond to biology and general science. * Ample, useful end-of-chapter problems.* Excellent Coverage of Computational Fluid Dynamics.* Coverage of Turbulent Flows.* Solutions Manual available.

    Known for its carefully developed, thoroughly integrated, step-by-step approach to problem solving, General Chemistry helps students master quantitative skills and build a lasting conceptual understanding of key chemical concepts. The Ninth Edition retains this hallmark approach and builds upon the conceptual focus through key new features and revisions.

    Organized around the central concept of a vector space, the book includes numerous physical applications in the body of the text as well as many problems of a physical nature. It is also one of the purposes of this book to introduce the physicist to the language and style of mathematics as well as the content of those particular subjects with contemporary relevance in physics.Chapters 1 and 2 are devoted to the mathematics of classical physics. Chapters 3, 4 and 5 — the backbone of the book — cover the theory of vector spaces. Chapter 6 covers analytic function theory. In chapters 7, 8, and 9 the authors take up several important techniques of theoretical physics — the Green's function method of solving differential and partial differential equations, and the theory of integral equations. Chapter 10 introduces the theory of groups. The authors have included a large selection of problems at the end of each chapter, some illustrating or extending mathematical points, others stressing physical application of techniques developed in the text.Essentially self-contained, the book assumes only the standard undergraduate preparation in physics and mathematics, i.e. intermediate mechanics, electricity and magnetism, introductory quantum mechanics, advanced calculus and differential equations. The text may be easily adapted for a one-semester course at the graduate or advanced undergraduate level.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    How did the table fork acquire a fourth tine?  What advantage does the Phillips-head screw have over its single-grooved predecessor? Why does the paper clip look the way it does? What makes Scotch tape Scotch?   In this delightful book Henry Petroski takes a microscopic look at artifacts that most of us count on but rarely contemplate, including such icons of the everyday as pins, Post-its, and fast-food "clamshell" containers.  At the same time, he offers a convincing new theory of technological innovation as a response to the perceived failures of existing products—suggesting that irritation, and not necessity, is the mother of invention.

    Readable and stimulating, yet well-balanced and in-depth, this text tells the story of evolution, from the history of the study to the most revent developments in evolutionary theory. The third edition of this successful textbook features updates and extensive new coverage. The sections on adaptation and diversity have been reorganized for improved clarity and flow, and a completely updated section on the evolution of sex and the inclusion of more plant examples have all helped to shape this new edition. Evolution also features strong, balanced coverage of population genetics, and scores of new applied plant and animal examples make this edition even more accessible and engaging. Dedicated website - provides an interactive experience of the book, with illustrations downloadable to PowerPoint, and a full supplemental package complementing the book - www.blackwellpublishing.com/ridley. Margin icons - indicate where there is relevant information included in the dedicated website. Two new chapters - one on evolutionary genomics and one on evolution and development bring state-of-the-art information to the coverage of evolutionary study. Two kinds of boxes - one featuring practical applications and the other related information, supply added depth without interrupting the flow of the text. Margin comments - paraphrase and highlight key concepts. Study and review questions - help students review their understanding at the end of each chapter, while new challenge questions prompt students to synthesize the chapter concepts to reinforce the learning at a deeper level.

    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.

    Whenyou upgrade to Extra Class, you gain access to the entire Amateur Radio frequency spectrum. Ues this book to ace the top-level ham radio licensing exam. Our expert instruction will lead you through all of the knowledge you need to pass the exam: rules, specific operating skills and more advanced electronics theory.

    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.

    Intuition and computational abilities are stressed. Original material on DE and multiple integrals has been expanded.

    • Mercury thermometers are prohibited on airplanes for a crucial reason.• Kryptonite bicycle locks are easily broken.“Things fall apart” is more than a poetic insight—it is a fundamental property of the physical world. Why Things Break explores the fascinating question of what holds things together (for a while), what breaks them apart, and why the answers have a direct bearing on our everyday lives.When Mark Eberhart was growing up in the 1960s, he learned that splitting an atom leads to a terrible explosion—which prompted him to worry that when he cut into a stick of butter, he would inadvertently unleash a nuclear cataclysm. Years later, as a chemistry professor, he remembered this childhood fear when he began to ponder the fact that we know more about how to split an atom than we do about how a pane of glass breaks.In Why Things Break, Eberhart leads us on a remarkable and entertaining exploration of all the cracks, clefts, fissures, and faults examined in the field of materials science and the many astonishing discoveries that have been made about everything from the explosion of the space shuttle Challenger to the crashing of your hard drive. Understanding why things break is crucial to modern life on every level, from personal safety to macroeconomics, but as Eberhart reveals here, it is also an area of cutting-edge science that is as provocative as it is illuminating.“An engaging personal account not just of the physics and chemistry of materials but of the ethics, economics, and politics of innovation, with delightful bonuses on topics from the origins of ‘ghostly’ noises in old houses to the amazing coevolution of armor and armor-piercing projectiles. If it ain’t broke, Mark Eberhart can tell you why—and explain equally well why a shatterproof world remains beyond our reach.”—Edward Tenner, author of Our Own Devices and Why Things Bite Back“I don’t remember a book that has taught me so much, nor previously encountering a teacher like the marvelous Mark Eberhart, who in Why Things Break provides enlightening and thoroughly captivating scientific explanations of subjects ranging from the structural failures leading to the sinking of the Titanic to everyday, no-less-fascinating topics such as the reason why, even at the same temperature, winter days always seem so much colder in Boston than in Denver.”—Richard Restak, M.D., author of Mozart’s Brain and The Fighter Pilot“Eberhart brings his insights to the reader by weaving personal anecdotes—from his childhood fear that cutting a stick of butter would release the energy of the atoms within to his arrival in Boston for an interview with MIT without a suitable winter coat—into a fascinating discussion of the forces that hold atoms and molecules together. A lively, unvarnished look at chemistry on the cutting edge.” —Kirkus Reviews