Linear Algebra


Georgi E. Shilov - 1971
    Shilov, Professor of Mathematics at the Moscow State University, covers determinants, linear spaces, systems of linear equations, linear functions of a vector argument, coordinate transformations, the canonical form of the matrix of a linear operator, bilinear and quadratic forms, Euclidean spaces, unitary spaces, quadratic forms in Euclidean and unitary spaces, finite-dimensional algebras and their representations, with an appendix on categories of finite-dimensional spaces.The author begins with elementary material and goes easily into the advanced areas, covering all the standard topics of an advanced undergraduate or beginning graduate course. The material is presented in a consistently clear style. Problems are included, with a full section of hints and answers in the back.Keeping in mind the unity of algebra, geometry and analysis in his approach, and writing practically for the student who needs to learn techniques, Professor Shilov has produced one of the best expositions on the subject. Because it contains an abundance of problems and examples, the book will be useful for self-study as well as for the classroom.

Mathematical Proofs: A Transition to Advanced Mathematics


Gary Chartrand - 2002
    This text introduces students to proof techniques and writing proofs of their own. As such, it is an introduction to the mathematics enterprise, providing solid introductions to relations, functions, and cardinalities of sets.

Computational Complexity


Christos H. Papadimitriou - 1993
    It offers a comprehensive and accessible treatment of the theory of algorithms and complexity—the elegant body of concepts and methods developed by computer scientists over the past 30 years for studying the performance and limitations of computer algorithms. The book is self-contained in that it develops all necessary mathematical prerequisites from such diverse fields such as computability, logic, number theory and probability.

Tell Me The Odds: A 15 Page Introduction To Bayes Theorem


Scott Hartshorn - 2017
    Essentially, you make an initial guess, and then get more data to improve it. Bayes Theorem, or Bayes Rule, has a ton of real world applications, from estimating your risk of a heart attack to making recommendations on Netflix But It Isn't That Complicated This book is a short introduction to Bayes Theorem. It is only 15 pages long, and is intended to show you how Bayes Theorem works as quickly as possible. The examples are intentionally kept simple to focus solely on Bayes Theorem without requiring that the reader know complicated probability distributions. If you want to learn the basics of Bayes Theorem as quickly as possible, with some easy to duplicate examples, this is a good book for you.

Solid State Physics


Neil W. Ashcroft - 1976
    This book provides an introduction to the field of solid state physics for undergraduate students in physics, chemistry, engineering, and materials science.

Elementary Differential Equations And Boundary Value Problems


William E. Boyce - 1996
    Clear explanations are detailed with many current examples.

The Frege Reader


Gottlob Frege - 1997
    It is intended to provide the essential primary texts for students of logic, metaphysics and philosophy of language.It contains, in particular, Frege's four essays 'Function and Concept', 'On Sinn and Bedeutung', 'On Concept and Object' and 'Thought', and new translations of key parts of the Begriffschrift, Grundlagen and Grundgesetze. Additional selections have also been made from his Collected Papers, Posthumous Writings and Correspondence. The editor's introduction provides an overview of the development and significance of Frege's philosophy, highlighting some of the main issues of interpretation. Footnotes, appendices and other editorial material have been supplied to facilitate understanding of the works of one of the central figures in modern philosophy.

Networks: An Introduction


M.E.J. Newman - 2010
    The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.

Physical Chemistry


Peter Atkins - 1978
    With its modern emphasis on the molecular view of physical chemistry, its wealth of contemporary applications (in the new "Impact on" features), vivid full-color presentation, and dynamic new media tools, the thoroughly revised new edition is again the most modern, most effective full-length textbook available for the physical chemistry classroom. NOW AVAILABLE IN SPLIT VOLUMESFor maximum flexibility in your physical chemistry course, this text isnow offered as a traditional or in two volumes.• Volume 1:  Thermodynamics and Kinetics (ISBN 0-7167-8567-6)• Volume 2:  Quantum Chemistry, Spectroscopy, and StatisticalThermodynamics (ISBN 0-7167-8569-2)See Table of Contents for the contents of each volume.

Elements Of Discrete Mathematics: Solutions Manual


Chung Laung Liu - 1999
    

Advanced Differential Equations


M.D. Raisinghania - 1995
    

Thermodynamics


Enrico Fermi - 1956
    Based on a course of lectures delivered by the author at Columbia University, the text is elementary in treatment and remarkable for its clarity and organization. Although it is assumed that the reader is familiar with the fundamental facts of thermometry and calorimetry, no advanced mathematics beyond calculus is assumed.Partial contents: thermodynamic systems, the first law of thermodynamics (application, adiabatic transformations), the second law of thermodynamics (Carnot cycle, absolute thermodynamic temperature, thermal engines), the entropy (properties of cycles, entropy of a system whose states can be represented on a (V, p) diagram, Clapeyron and Van der Waals equations), thermodynamic potentials (free energy, thermodynamic potential at constant pressure, the phase rule, thermodynamics of the reversible electric cell), gaseous reactions (chemical equilibria in gases, Van't Hoff reaction box, another proof of the equation of gaseous equilibria, principle of Le Chatelier), the thermodynamics of dilute solutions (osmotic pressure, chemical equilibria in solutions, the distribution of a solute between 2 phases vapor pressure, boiling and freezing points), the entropy constant (Nernst's theorem, thermal ionization of a gas, thermionic effect, etc.).

Pure Mathematics 1: Advanced Level Mathematics


Hugh Neill - 2002
    Pure Mathematics 1 corresponds to unit P1. It covers quadratics, functions, coordinate geometry, circular measure, trigonometry, vectors, series, differentiation and integration.

Classical Mechanics


John R. Taylor - 2003
    John Taylor has brought to his new book, Classical Mechanics, all of the clarity and insight that made his introduction to Error Analysis a best-selling text.

Data Analysis Using Regression and Multilevel/Hierarchical Models


Andrew Gelman - 2006
    The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http: //www.stat.columbia.edu/ gelman/arm/