Linear Equations; Vector Spaces; Linear Transformations; Polynomials; Determinants; Elementary canonical Forms; Rational and Jordan Forms; Inner Product Spaces; Operators on Inner Product Spaces; Bilinear Forms For all readers interested in linear algebra.

    Designed for the junior- level astrophysics course, each topic is approached in the context of the major unresolved questions in astrophysics. The core chapters have been designed for a course in stellar structure and evolution, while the extended chapters provide additional coverage of the solar system, galactic structure, dynamics, evolution, and cosmology. * Two versions of this text are available: An Introduction to Modern Stellar Astrophysics, (Chapters 1-17), and An Introduction to Modern Astrophysics, (Chapters 1-28). * Computer programs included with the text allow students to explore the physics of stars and galaxies. * In designing a curriculum, instructors can combine core and extended chapters with the optional advanced sections so as to meet their individual goals. * Up-to-date coverage of current astrophysical discoveries are included. * This text emphasizes computational physics, including computer problems and on-line programs. * This text also includes a selection of over 500 problems. For additional information and computer codes to be used

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    Bellow. Strauss. Friedman. The University of Chicago has been the home of some of the most important thinkers of the modern age. But perhaps no name has been spoken with more respect than Turabian. The dissertation secretary at Chicago for decades, Kate Turabian literally wrote the book on the successful completion and submission of the student paper. Her Manual for Writers of Research Papers, Theses, and Dissertations, created from her years of experience with research projects across all fields, has sold more than seven million copies since it was first published in 1937.Now, with this seventh edition, Turabian’s Manual has undergone its most extensive revision, ensuring that it will remain the most valuable handbook for writers at every level—from first-year undergraduates, to dissertation writers apprehensively submitting final manuscripts, to senior scholars who may be old hands at research and writing but less familiar with new media citation styles. Gregory G. Colomb, Joseph M. Williams, and the late Wayne C. Booth—the gifted team behind The Craft of Research—and the University of Chicago Press Editorial Staff combined their wide-ranging expertise to remake this classic resource. They preserve Turabian’s clear and practical advice while fully embracing the new modes of research, writing, and source citation brought about by the age of the Internet.Booth, Colomb, and Williams significantly expand the scope of previous editions by creating a guide, generous in length and tone, to the art of research and writing. Growing out of the authors’ best-selling Craft of Research, this new section provides students with an overview of every step of the research and writing process, from formulating the right questions to reading critically to building arguments and revising drafts. This leads naturally to the second part of the Manual for Writers, which offers an authoritative overview of citation practices in scholarly writing, as well as detailed information on the two main citation styles (“notes-bibliography” and “author-date”). This section has been fully revised to reflect the recommendations of the fifteenth edition of The Chicago Manual of Style and to present an expanded array of source types and updated examples, including guidance on citing electronic sources.The final section of the book treats issues of style—the details that go into making a strong paper. Here writers will find advice on a wide range of topics, including punctuation, table formatting, and use of quotations. The appendix draws together everything writers need to know about formatting research papers, theses, and dissertations and preparing them for submission. This material has been thoroughly vetted by dissertation officials at colleges and universities across the country.This seventh edition of Turabian’s Manual for Writers of Research Papers, Theses, and Dissertations is a classic reference revised for a new age. It is tailored to a new generation of writers using tools its original author could not have imagined—while retaining the clarity and authority that generations of scholars have come to associate with the name Turabian.

    But what if you could have your computer do them for you?In "Automate the Boring Stuff with Python," you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to: Search for text in a file or across multiple filesCreate, update, move, and rename files and foldersSearch the Web and download online contentUpdate and format data in Excel spreadsheets of any sizeSplit, merge, watermark, and encrypt PDFsSend reminder emails and text notificationsFill out online formsStep-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.Don't spend your time doing work a well-trained monkey could do. Even if you've never written a line of code, you can make your computer do the grunt work. Learn how in "Automate the Boring Stuff with Python.""

    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.

    of Wisconsin-Madison) and Wichern (Texas A&M U.) present the newest edition of this college text on the statistical methods for describing and analyzing multivariate data, designed for students who have taken two or more statistics courses. The fifth edition includes the addition of seve

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    Called by some, "the theory of everything," superstrings may solve a problem that has eluded physicists for the past 50 years, the final unification of the two great theories of the twentieth century, general relativity and quantum field theory. Now, here is a thoroughly revised, second edition of a course-tested comprehensive introductory graduate text on superstrings which stresses the most current areas of interest, not covered in other presentations, including: - Four-dimensional superstrings - Kac-Moody algebras - Teichm�ller spaces and Calabi-Yau manifolds - M-theory Membranes and D-branes - Duality and BPS relations - Matrix models The book begins with a simple discussion of point particle theory, and uses Feynman path integrals to unify the presentation of superstrings. It has been updated throughout, and three new chapters on M-theory have been added. Prerequisites are an acquaintance with quantum mechanics and relativity.

    The gold standard for attaining security is cryptography because it provides the most reliable tools for storing or transmitting digital information. Written by Niels Ferguson, lead cryptographer for Counterpane, Bruce Schneier's security company, and Bruce Schneier himself, this is the much anticipated follow-up book to Schneier's seminal encyclopedic reference, Applied Cryptography, Second Edition (0-471-11709-9), which has sold more than 150,000 copies. Niels Ferguson (Amsterdam, Netherlands) is a cryptographic engineer and consultant at Counterpane Internet Security. He has extensive experience in the creation and design of security algorithms, protocols, and multinational security infrastructures. Previously, Ferguson was a cryptographer for DigiCash and CWI. At CWI he developed the first generation of off-line payment protocols. He has published numerous scientific papers. Bruce Schneier (Minneapolis, MN) is Founder and Chief Technical Officer at Counterpane Internet Security, a managed-security monitoring company. He is also the author of Secrets and Lies: Digital Security in a Networked World (0-471-25311-1).

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    Following closely behind is y, or gamma, a constant that arises in many mathematical areas yet maintains a profound sense of mystery. In a tantalizing blend of history and mathematics, Julian Havil takes the reader on a journey through logarithms and the harmonic series, the two defining elements of gamma, toward the first account of gamma's place in mathematics. Introduced by the Swiss mathematician Leonhard Euler (1707-1783), who figures prominently in this book, gamma is defined as the limit of the sum of 1 + 1/2 + 1/3 + . . . Up to 1/n, minus the natural logarithm of n--the numerical value being 0.5772156. . . . But unlike its more celebrated colleagues π and e, the exact nature of gamma remains a mystery--we don't even know if gamma can be expressed as a fraction. Among the numerous topics that arise during this historical odyssey into fundamental mathematical ideas are the Prime Number Theorem and the most important open problem in mathematics today--the Riemann Hypothesis (though no proof of either is offered!). Sure to be popular with not only students and instructors but all math aficionados, Gamma takes us through countries, centuries, lives, and works, unfolding along the way the stories of some remarkable mathematics from some remarkable mathematicians.-- "Notices of the American Mathematical Society"

    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.

    Topics include: Limits, Derivatives, and Integrations.

    In the paper, Wigner observed that the mathematical structure of a physical theory often points the way to further advances in that theory and even to empirical predictions.