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Author: Eric Connally Publisher: John Wiley & Sons ISBN: 1119498317 Category : Mathematics Languages : en Pages : 546
Book Description
An accessible Precalculus text with concepts, examples, and problems The sixth edition of Functions Modeling Change: A Preparation for Calculus helps students establish a foundation for studying Calculus. The text covers key Precalculus topics, examples, and problems. Chapters examine linear, quadratic, logarithmic, exponential, polynomial, and rational functions. They also explore trigonometry and trigonometric Identities, plus vectors and matrices. The end of each chapter offers details on how students can strengthen their knowledge about the topics covered.
Author: Eric Connally Publisher: John Wiley & Sons ISBN: 1119498317 Category : Mathematics Languages : en Pages : 546
Book Description
An accessible Precalculus text with concepts, examples, and problems The sixth edition of Functions Modeling Change: A Preparation for Calculus helps students establish a foundation for studying Calculus. The text covers key Precalculus topics, examples, and problems. Chapters examine linear, quadratic, logarithmic, exponential, polynomial, and rational functions. They also explore trigonometry and trigonometric Identities, plus vectors and matrices. The end of each chapter offers details on how students can strengthen their knowledge about the topics covered.
Author: Marc Peter Deisenroth Publisher: Cambridge University Press ISBN: 1108569323 Category : Computers Languages : en Pages : 392
Book Description
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.
Author: Mary L. Boas Publisher: John Wiley & Sons ISBN: 9788126508105 Category : Mathematical physics Languages : en Pages : 868
Book Description
Market_Desc: · Physicists and Engineers· Students in Physics and Engineering Special Features: · Covers everything from Linear Algebra, Calculus, Analysis, Probability and Statistics, to ODE, PDE, Transforms and more· Emphasizes intuition and computational abilities· Expands the material on DE and multiple integrals· Focuses on the applied side, exploring material that is relevant to physics and engineering· Explains each concept in clear, easy-to-understand steps About The Book: The book provides a comprehensive introduction to the areas of mathematical physics. It combines all the essential math concepts into one compact, clearly written reference. This book helps readers gain a solid foundation in the many areas of mathematical methods in order to achieve a basic competence in advanced physics, chemistry, and engineering.
Author: Alan Garfinkel Publisher: Springer ISBN: 3319597310 Category : Mathematics Languages : en Pages : 456
Book Description
This book develops the mathematical tools essential for students in the life sciences to describe interacting systems and predict their behavior. From predator-prey populations in an ecosystem, to hormone regulation within the body, the natural world abounds in dynamical systems that affect us profoundly. Complex feedback relations and counter-intuitive responses are common in nature; this book develops the quantitative skills needed to explore these interactions. Differential equations are the natural mathematical tool for quantifying change, and are the driving force throughout this book. The use of Euler’s method makes nonlinear examples tractable and accessible to a broad spectrum of early-stage undergraduates, thus providing a practical alternative to the procedural approach of a traditional Calculus curriculum. Tools are developed within numerous, relevant examples, with an emphasis on the construction, evaluation, and interpretation of mathematical models throughout. Encountering these concepts in context, students learn not only quantitative techniques, but how to bridge between biological and mathematical ways of thinking. Examples range broadly, exploring the dynamics of neurons and the immune system, through to population dynamics and the Google PageRank algorithm. Each scenario relies only on an interest in the natural world; no biological expertise is assumed of student or instructor. Building on a single prerequisite of Precalculus, the book suits a two-quarter sequence for first or second year undergraduates, and meets the mathematical requirements of medical school entry. The later material provides opportunities for more advanced students in both mathematics and life sciences to revisit theoretical knowledge in a rich, real-world framework. In all cases, the focus is clear: how does the math help us understand the science?
Author: Eric Connally
Author: Eric Connally
Author: Eric Connally
Author: 
Author: Eric Connally
Author: Connally
Author: Eric Connally
Author: Connally
Author: Marc Peter Deisenroth
Author: Mary L. Boas
Author: Alan Garfinkel