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EBOOK: Foundation Mathematics for Non-Mathematicians

EBOOK: Foundation Mathematics for Non-Mathematicians
Author: Milo Shott
Publisher: McGraw-Hill Education (UK)
Total Pages: 242
Release: 1990-01-16
Genre: Study Aids
ISBN: 0335232779

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This book is for students who either never obtained any formal qualifications in mathematics, or whose knowledge became rusty through prolonged lack of use. It explains mathematical concepts and topics which are prerequisites for a student embarking on any science or other numerically based course in further and higher education. The text contains many worked examples, illustrations and exercises with solutions to reinforce understanding of the material. The emphasis is on a user-friendly approach and simplicity of style - which makes the book easy to study on its own, without any editorial help.


Foundation Mathematics For Non-Mathematicians

Foundation Mathematics For Non-Mathematicians
Author: Shott, Milo
Publisher: McGraw-Hill Education (UK)
Total Pages: 242
Release: 1990-01-01
Genre: Education
ISBN: 0335092128

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This book is for students who either never obtained any formal qualifications in mathematics, or whose knowledge became rusty through prolonged lack of use. It explains mathematical concepts and topics which are prerequisites for a student embarking on any science or other numerically based course in further and higher education. The text contains many worked examples, illustrations and exercises with solutions to reinforce understanding of the material. The emphasis is on a user-friendly approach and simplicity of style - which makes the book easy to study on its own, without any editorial help.


The Foundations of Mathematics

The Foundations of Mathematics
Author: Kenneth Kunen
Publisher:
Total Pages: 251
Release: 2009
Genre: Mathematics
ISBN: 9781904987147

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Mathematical logic grew out of philosophical questions regarding the foundations of mathematics, but logic has now outgrown its philosophical roots, and has become an integral part of mathematics in general. This book is designed for students who plan to specialize in logic, as well as for those who are interested in the applications of logic to other areas of mathematics. Used as a text, it could form the basis of a beginning graduate-level course. There are three main chapters: Set Theory, Model Theory, and Recursion Theory. The Set Theory chapter describes the set-theoretic foundations of all of mathematics, based on the ZFC axioms. It also covers technical results about the Axiom of Choice, well-orderings, and the theory of uncountable cardinals. The Model Theory chapter discusses predicate logic and formal proofs, and covers the Completeness, Compactness, and Lowenheim-Skolem Theorems, elementary submodels, model completeness, and applications to algebra. This chapter also continues the foundational issues begun in the set theory chapter. Mathematics can now be viewed as formal proofs from ZFC. Also, model theory leads to models of set theory. This includes a discussion of absoluteness, and an analysis of models such as H( ) and R( ). The Recursion Theory chapter develops some basic facts about computable functions, and uses them to prove a number of results of foundational importance; in particular, Church's theorem on the undecidability of logical consequence, the incompleteness theorems of Godel, and Tarski's theorem on the non-definability of truth.


Mathematical Foundations of Elasticity

Mathematical Foundations of Elasticity
Author: Jerrold E. Marsden
Publisher: Courier Corporation
Total Pages: 578
Release: 2012-10-25
Genre: Technology & Engineering
ISBN: 0486142272

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Graduate-level study approaches mathematical foundations of three-dimensional elasticity using modern differential geometry and functional analysis. It presents a classical subject in a modern setting, with examples of newer mathematical contributions. 1983 edition.


The Foundations of Mathematics in the Theory of Sets

The Foundations of Mathematics in the Theory of Sets
Author: John P. Mayberry
Publisher: Cambridge University Press
Total Pages: 454
Release: 2000
Genre: Mathematics
ISBN: 9780521770347

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This 2001 book will appeal to mathematicians and philosophers interested in the foundations of mathematics.


Mathematical Foundations of Imaging, Tomography and Wavefield Inversion

Mathematical Foundations of Imaging, Tomography and Wavefield Inversion
Author: Anthony J. Devaney
Publisher: Cambridge University Press
Total Pages: 537
Release: 2012-06-21
Genre: Science
ISBN: 1139510142

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Inverse problems are of interest and importance across many branches of physics, mathematics, engineering and medical imaging. In this text, the foundations of imaging and wavefield inversion are presented in a clear and systematic way. The necessary theory is gradually developed throughout the book, progressing from simple wave equation based models to vector wave models. By combining theory with numerous MATLAB based examples, the author promotes a complete understanding of the material and establishes a basis for real world applications. Key topics of discussion include the derivation of solutions to the inhomogeneous and homogeneous Helmholtz equations using Green function techniques; the propagation and scattering of waves in homogeneous and inhomogeneous backgrounds; and the concept of field time reversal. Bridging the gap between mathematics and physics, this multidisciplinary book will appeal to graduate students and researchers alike. Additional resources including MATLAB codes and solutions are available online at www.cambridge.org/9780521119740.


Mathematical Foundations of Neuroscience

Mathematical Foundations of Neuroscience
Author: G. Bard Ermentrout
Publisher: Springer Science & Business Media
Total Pages: 434
Release: 2010-07-01
Genre: Mathematics
ISBN: 0387877088

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This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University.


Mathematics for Machine Learning

Mathematics for Machine Learning
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Total Pages: 392
Release: 2020-04-23
Genre: Computers
ISBN: 1108569323

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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.


Sets for Mathematics

Sets for Mathematics
Author: F. William Lawvere
Publisher: Cambridge University Press
Total Pages: 280
Release: 2003-01-27
Genre: Mathematics
ISBN: 9780521010603

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In this book, first published in 2003, categorical algebra is used to build a foundation for the study of geometry, analysis, and algebra.