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| the linear algebra survival guide illustrated with mathematica: The Linear Algebra Survival Guide Fred Szabo, 2015-02-27 The Linear Algebra Survival Guide offers a concise introduction to the difficult core topics of linear algebra, guiding you through the powerful graphic displays and visualization of Mathematica that make the most abstract theories seem simple - allowing you to tackle realistic problems using simple mathematical manipulations. This resource is therefore a guide to learning the content of Mathematica in a practical way, enabling you to manipulate potential solutions/outcomes, and learn creatively. No starting knowledge of the Mathematica system is required to use the book. Desktop, laptop, web-based versions of Mathematica are available on all major platforms. Mathematica Online for tablet and smartphone systems are also under development and increases the reach of the guide as a general reference, teaching and learning tool. - Includes computational oriented information that complements the essential topics in linear algebra. - Presents core topics in a simple, straightforward way with examples for exploring computational illustrations, graphics, and displays using Mathematica. - Provides numerous examples of short code in the text, which can be modified for use with exercises to develop graphics displays for teaching, learning, and demonstrations. |
| the linear algebra survival guide illustrated with mathematica: Medical Image Analysis Alejandro Frangi, Jerry Prince, Milan Sonka, 2023-09-20 Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. - An authoritative presentation of key concepts and methods from experts in the field - Sections clearly explaining key methodological principles within relevant medical applications - Self-contained chapters enable the text to be used on courses with differing structures - A representative selection of modern topics and techniques in medical image computing - Focus on medical image computing as an enabling technology to tackle unmet clinical needs - Presentation of traditional and machine learning approaches to medical image computing |
| the linear algebra survival guide illustrated with mathematica: Advances in Information and Communication Kohei Arai, 2024-03-16 The book is a valuable collection of papers presented in the Future of Information and Communications Conference (FICC), conducted by Science and Information Organization on 4–5 April 2024 in Berlin. It received a total of 401 paper submissions out of which 139 are published after careful double-blind peer-review. Renowned and budding scholars, academics, and distinguished members of the industry assembled under one roof to share their breakthrough research providing answers to many complex problems boggling the world. The topics fanned across various fields involving Communication, Data Science, Ambient Intelligence, Networking, Computing, Security, and Privacy. |
| the linear algebra survival guide illustrated with mathematica: Machine Learning, Optimization, and Data Science Giuseppe Nicosia, Varun Ojha, Sven Giesselbach, M. Panos Pardalos, Renato Umeton, 2025-03-03 The three-volume set LNAI 15508-15510 constitutes the refereed proceedings of the 10th International Conference on Machine Learning, Optimization, and Data Science, LOD 2024, held in Castiglione della Pescaia, Italy, during September 22–25, 2024. This year, in the LOD Proceedings decided to also include the papers of the fourth edition of the Symposium on Artificial Intelligence and Neuroscience (ACAIN 2024). The 79 full papers included in this book were carefully reviewed and selected from 127 submissions. The LOD 2024 proceedings focus on machine learning, deep learning, AI, computational optimization, neuroscience and big data that includes invited talks, tutorial talks, special sessions, industrial tracks, demonstrations and oral and poster presentations of refereed papers. |
| the linear algebra survival guide illustrated with mathematica: Linear Algebra with Mathematica Fred Szabo, 2000-02-14 Linear Algebra: An Introduction With Mathematica uses a matrix-based presentation and covers the standard topics any mathematician will need to understand linear algebra while using Mathematica. Development of analytical and computational skills is emphasized, and worked examples provide step-by-step methods for solving basic problems using Mathematica. The subject's rich pertinence to problem solving across disciplines is illustrated with applications in engineering, the natural sciences, computer animation, and statistics. Includes a thematic presentation of linear algebra Provides a systematic integration of Mathematica Encourages students to appreciate the benefits of mathematical rigor All exercises can be solved with Mathematica |
| the linear algebra survival guide illustrated with mathematica: MATHEMATICA kompakt Hans Benker, 2016-10-15 Dieses Buch bietet eine kurze und verständliche Einführung in das Softwarepaket MATHEMATICA und zeigt dessen Anwendung auf Problemstellungen aus der Ingenieurmathematik. Zunächst werden der Aufbau, die Arbeitsweise und die Möglichkeiten von MATHEMATICA näher beschrieben. Anschließend wird dieses Grundwissen auf die Grundlagen der Ingenieurmathematik, z.B. Matrizen, Differential- und Integralrechnung, angewendet. Der letzte Teil des Buches widmet sich den fortgeschrittenen Themen der Ingenieurmathematik. Dabei werden Differentialgleichungen, Transformationen, Optimierung, Wahrscheinlichkeitsrechnung und Statistik behandelt.Die Berechnungen werden jeweils ausführlich dargestellt und an zahlreichen Beispielen illustriert. |
| the linear algebra survival guide illustrated with mathematica: Linear Algebra with Maple, Lab Manual Fred Szabo, 2001-08-23 Linear Algebra: An Introduction Using MAPLE is a text for a first undergraduate course in linear algebra. All students majoring in mathematics, computer science, engineering, physics, chemistry, economics, statistics, actuarial mathematics and other such fields of study will benefit from this text. The presentation is matrix-based and covers the standard topics for a first course recommended by the Linear Algebra Curriculum Study Group. The aim of the book is to make linear algebra accessible to all college majors through a focused presentation of the material, enriched by interactive learning and teaching with MAPLE. Development of analytical and computational skills is emphasized throughout Worked examples provide step-by-step methods for solving basic problems using Maple The subject's rich pertinence to problem solving across disciplines is illustrated with applications in engineering, the natural sciences, computer animation, and statistics |
| the linear algebra survival guide illustrated with mathematica: Linear Algebra with Mathematica, Student Solutions Manual Fred Szabo, 2000-09-07 This book introduces interested readers, practitioners, and researchers to Mathematica$ methods for solving practical problems in linear algebra. It contains step-by-step solutions of problems in computer science, economics, engineering, mathematics, statistics, and other areas of application. Each chapter contains both elementary and more challenging problems, grouped by fields of application, and ends with a set of exercises. Selected answers are provided in an appendix. The book contains a glossary of definitions and theorem, as well as a summary of relevant Mathematica$ tools. Applications of Linear Algebra$ can be used both in laboratory sessions and as a source of take-home problems and projects. Concentrates on problem solving and aims to increase the readers' analytical skills Provides ample opportunities for applying theoretical results and transferring knowledge between different areas of application; Mathematica plays a key role in this process Makes learning fun and builds confidence Allows readers to tackle computationally challenging problems by minimizing the frustration caused by the arithmetic intricacies of numerical linear algebra |
| the linear algebra survival guide illustrated with mathematica: Linear Algebra with Mathematica Fred Szabo, 2009-12-02 Linear Algebra: An Introduction With Mathematica uses a matrix-based presentation and covers the standard topics any mathematician will need to understand linear algebra while using Mathematica. Development of analytical and computational skills is emphasized, and worked examples provide step-by-step methods for solving basic problems using Mathematica. The subject's rich pertinence to problem solving across disciplines is illustrated with applications in engineering, the natural sciences, computer animation, and statistics. |
| the linear algebra survival guide illustrated with mathematica: Actuaries' Survival Guide Fred Szabo, 2012-05-21 What would you like to do with your life? What career would allow you to fulfill your dreams of success? If you like mathematics-and the prospect of a highly mobile, international profession-consider becoming an actuary. Szabo's Actuaries' Survival Guide, Second Edition explains what actuaries are, what they do, and where they do it. It describes exciting combinations of ideas, techniques, and skills involved in the day-to-day work of actuaries. This second edition has been updated to reflect the rise of social networking and the internet, the progress toward a global knowledge-based economy, and the global expansion of the actuarial field that has occurred since the first edition. Includes details on the new structures of the Society of Actuaries' (SOA) and Casualty Actuarial Society (CAS) examinations, as well as sample questions and answers Presents an overview of career options, includes profiles of companies & agencies that employ actuaries. Provides a link between theory and practice and helps readers understand the blend of qualitative and quantitative skills and knowledge required to succeed in actuarial exams Includes insights provided by over 50 actuaries and actuarial students about the actuarial profession Author Fred Szabo has directed the Actuarial Co-op Program at Concordia for over fifteen years |
| the linear algebra survival guide illustrated with mathematica: Visualizing Quaternions Andrew J. Hanson, 2006-02-06 Introduced 160 years ago as an attempt to generalize complex numbers to higher dimensions, quaternions are now recognized as one of the most important concepts in modern computer graphics. They offer a powerful way to represent rotations and compared to rotation matrices they use less memory, compose faster, and are naturally suited for efficient interpolation of rotations. Despite this, many practitioners have avoided quaternions because of the mathematics used to understand them, hoping that some day a more intuitive description will be available.The wait is over. Andrew Hanson's new book is a fresh perspective on quaternions. The first part of the book focuses on visualizing quaternions to provide the intuition necessary to use them, and includes many illustrative examples to motivate why they are important—a beautiful introduction to those wanting to explore quaternions unencumbered by their mathematical aspects. The second part covers the all-important advanced applications, including quaternion curves, surfaces, and volumes. Finally, for those wanting the full story of the mathematics behind quaternions, there is a gentle introduction to their four-dimensional nature and to Clifford Algebras, the all-encompassing framework for vectors and quaternions. - Richly illustrated introduction for the developer, scientist, engineer, or student in computer graphics, visualization, or entertainment computing. - Covers both non-mathematical and mathematical approaches to quaternions. |
| the linear algebra survival guide illustrated with mathematica: Whitaker's Books in Print , 1998 |
| the linear algebra survival guide illustrated with mathematica: Linear Algebra Theodore Shifrin, Malcolm Adams, 2010-07-30 Linear Algebra: A Geometric Approach, Second Edition, presents the standard computational aspects of linear algebra and includes a variety of intriguing interesting applications that would be interesting to motivate science and engineering students, as well as help mathematics students make the transition to more abstract advanced courses. The text guides students on how to think about mathematical concepts and write rigorous mathematical arguments. |
| the linear algebra survival guide illustrated with mathematica: Bilinear Control Systems David Elliott, 2009-09-01 The mathematical theory of control became a ?eld of study half a century ago in attempts to clarify and organize some challenging practical problems and the methods used to solve them. It is known for the breadth of the mathematics it uses and its cross-disciplinary vigor. Its literature, which can befoundinSection93ofMathematicalReviews,wasatonetimedominatedby the theory of linear control systems, which mathematically are described by linear di?erential equations forced by additive control inputs. That theory led to well-regarded numerical and symbolic computational packages for control analysis and design. Nonlinear control problems are also important; in these either the - derlying dynamical system is nonlinear or the controls are applied in a n- additiveway.Thelastfourdecadeshaveseenthedevelopmentoftheoretical work on nonlinear control problems based on di?erential manifold theory, nonlinear analysis, and several other mathematical disciplines. Many of the problems that had been solved in linear control theory, plus others that are new and distinctly nonlinear, have been addressed; some resulting general de?nitions and theorems are adapted in this book to the bilinear case. |
| the linear algebra survival guide illustrated with mathematica: A First Course in Scientific Computing Rubin Landau, 2011-10-30 This book offers a new approach to introductory scientific computing. It aims to make students comfortable using computers to do science, to provide them with the computational tools and knowledge they need throughout their college careers and into their professional careers, and to show how all the pieces can work together. Rubin Landau introduces the requisite mathematics and computer science in the course of realistic problems, from energy use to the building of skyscrapers to projectile motion with drag. He is attentive to how each discipline uses its own language to describe the same concepts and how computations are concrete instances of the abstract. Landau covers the basics of computation, numerical analysis, and programming from a computational science perspective. The first part of the printed book uses the problem-solving environment Maple as its context, with the same material covered on the accompanying CD as both Maple and Mathematica programs; the second part uses the compiled language Java, with equivalent materials in Fortran90 on the CD; and the final part presents an introduction to LaTeX replete with sample files. Providing the essentials of computing, with practical examples, A First Course in Scientific Computing adheres to the principle that science and engineering students learn computation best while sitting in front of a computer, book in hand, in trial-and-error mode. Not only is it an invaluable learning text and an essential reference for students of mathematics, engineering, physics, and other sciences, but it is also a consummate model for future textbooks in computational science and engineering courses. A broad spectrum of computing tools and examples that can be used throughout an academic career Practical computing aimed at solving realistic problems Both symbolic and numerical computations A multidisciplinary approach: science + math + computer science Maple and Java in the book itself; Mathematica, Fortran90, Maple and Java on the accompanying CD in an interactive workbook format |
| the linear algebra survival guide illustrated with mathematica: Modeling Life Alan Garfinkel, Jane Shevtsov, Yina Guo, 2017-09-06 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? |
| the linear algebra survival guide illustrated with mathematica: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| the linear algebra survival guide illustrated with mathematica: Bandit Algorithms Tor Lattimore, Csaba Szepesvári, 2020-07-16 A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems. |
| the linear algebra survival guide illustrated with mathematica: A Guide to MATLAB Brian R. Hunt, Ronald L. Lipsman, Jonathan M. Rosenberg, 2001-08-06 This book is a short, focused introduction to MATLAB and should be useful to both beginning and experienced users. |
| the linear algebra survival guide illustrated with mathematica: Forthcoming Books Rose Arny, 1998-04 |
| the linear algebra survival guide illustrated with mathematica: Generalized Linear Models with Random Effects Youngjo Lee, John A. Nelder, Yudi Pawitan, 2006-07-13 Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors. Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis. |
| the linear algebra survival guide illustrated with mathematica: Nonlinear Dynamics and Chaos Steven H. Strogatz, 2018-05-04 This textbook is aimed at newcomers to nonlinear dynamics and chaos, especially students taking a first course in the subject. The presentation stresses analytical methods, concrete examples, and geometric intuition. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization, fractals, and strange attractors. |
| the linear algebra survival guide illustrated with mathematica: A Biologist's Guide to Mathematical Modeling in Ecology and Evolution Sarah P. Otto, Troy Day, 2011-09-19 Thirty years ago, biologists could get by with a rudimentary grasp of mathematics and modeling. Not so today. In seeking to answer fundamental questions about how biological systems function and change over time, the modern biologist is as likely to rely on sophisticated mathematical and computer-based models as traditional fieldwork. In this book, Sarah Otto and Troy Day provide biology students with the tools necessary to both interpret models and to build their own. The book starts at an elementary level of mathematical modeling, assuming that the reader has had high school mathematics and first-year calculus. Otto and Day then gradually build in depth and complexity, from classic models in ecology and evolution to more intricate class-structured and probabilistic models. The authors provide primers with instructive exercises to introduce readers to the more advanced subjects of linear algebra and probability theory. Through examples, they describe how models have been used to understand such topics as the spread of HIV, chaos, the age structure of a country, speciation, and extinction. Ecologists and evolutionary biologists today need enough mathematical training to be able to assess the power and limits of biological models and to develop theories and models themselves. This innovative book will be an indispensable guide to the world of mathematical models for the next generation of biologists. A how-to guide for developing new mathematical models in biology Provides step-by-step recipes for constructing and analyzing models Interesting biological applications Explores classical models in ecology and evolution Questions at the end of every chapter Primers cover important mathematical topics Exercises with answers Appendixes summarize useful rules Labs and advanced material available |
| the linear algebra survival guide illustrated with mathematica: Books In Print 2004-2005 Ed Bowker Staff, Staff Bowker, Ed, 2004 |
| the linear algebra survival guide illustrated with mathematica: The U.S. Technology Skills Gap Gary J. Beach, 2013-07-10 Is a widening “skills gap” in science and math education threatening America’s future? That is the seminal question addressed in The U.S. Technology Skills Gap, a comprehensive 104-year review of math and science education in America. Some claim this “skills gap” is “equivalent to a permanent national recession” while others cite how the gap threatens America’s future economic, workforce employability and national security. This much is sure: America’s math and science skills gap is, or should be, an issue of concern for every business and information technology executive in the United States and The U.S Technology Skills Gap is the how-to-get involved guidebook for those executives laying out in a compelling chronologic format: The history of the science and math skills gap in America Explanation of why decades of astute warnings were ignored Inspiring examples of private company efforts to supplement public education A pragmatic 10-step action plan designed to solve the problem And a tantalizing theory of an obscure Japanese physicist that suggests America’s days as the global scientific leader are numbered Engaging and indispensable, The U.S. Technology Skills Gap is essential reading for those eager to see America remain a relevant global power in innovation and invention in the years ahead. |
| the linear algebra survival guide illustrated with mathematica: The British National Bibliography Arthur James Wells, 2001 |
| the linear algebra survival guide illustrated with mathematica: Modern Statistics with R Måns Thulin, 2024 The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com. |
| the linear algebra survival guide illustrated with mathematica: Statistical Rethinking Richard McElreath, 2016-01-05 Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas. |
| the linear algebra survival guide illustrated with mathematica: Algorithms for Optimization Mykel J. Kochenderfer, Tim A. Wheeler, 2019-03-12 A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals. |
| the linear algebra survival guide illustrated with mathematica: Mathematical Models in Biology Elizabeth Spencer Allman, John A. Rhodes, 2004 This introductory textbook on mathematical biology focuses on discrete models across a variety of biological subdisciplines. Biological topics treated include linear and non-linear models of populations, Markov models of molecular evolution, phylogenetic tree construction, genetics, and infectious disease models. The coverage of models of molecular evolution and phylogenetic tree construction from DNA sequence data is unique among books at this level. Computer investigations with MATLAB are incorporated throughout, in both exercises and more extensive projects, to give readers hands-on experience with the mathematical models developed. MATLAB programs accompany the text. Mathematical tools, such as matrix algebra, eigenvector analysis, and basic probability, are motivated by biological models and given self-contained developments, so that mathematical prerequisites are minimal. |
| the linear algebra survival guide illustrated with mathematica: Mathematics for Neuroscientists Fabrizio Gabbiani, Steven James Cox, 2017-02-04 Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neural components of the brain from ion channels to single neurons, neural networks and their relation to behavior. The book contains more than 200 figures generated using Matlab code available to the student and scholar. Mathematical concepts are introduced hand in hand with neuroscience, emphasizing the connection between experimental results and theory. - Fully revised material and corrected text - Additional chapters on extracellular potentials, motion detection and neurovascular coupling - Revised selection of exercises with solutions - More than 200 Matlab scripts reproducing the figures as well as a selection of equivalent Python scripts |
| the linear algebra survival guide illustrated with mathematica: Chebyshev and Fourier Spectral Methods John P. Boyd, 2001-12-03 Completely revised text focuses on use of spectral methods to solve boundary value, eigenvalue, and time-dependent problems, but also covers Hermite, Laguerre, rational Chebyshev, sinc, and spherical harmonic functions, as well as cardinal functions, linear eigenvalue problems, matrix-solving methods, coordinate transformations, methods for unbounded intervals, spherical and cylindrical geometry, and much more. 7 Appendices. Glossary. Bibliography. Index. Over 160 text figures. |
| the linear algebra survival guide illustrated with mathematica: Mathematical Modeling and Simulation Kai Velten, 2009-06-01 This concise and clear introduction to the topic requires only basic knowledge of calculus and linear algebra - all other concepts and ideas are developed in the course of the book. Lucidly written so as to appeal to undergraduates and practitioners alike, it enables readers to set up simple mathematical models on their own and to interpret their results and those of others critically. To achieve this, many examples have been chosen from various fields, such as biology, ecology, economics, medicine, agricultural, chemical, electrical, mechanical and process engineering, which are subsequently discussed in detail. Based on the author`s modeling and simulation experience in science and engineering and as a consultant, the book answers such basic questions as: What is a mathematical model? What types of models do exist? Which model is appropriate for a particular problem? What are simulation, parameter estimation, and validation? The book relies exclusively upon open-source software which is available to everybody free of charge. The entire book software - including 3D CFD and structural mechanics simulation software - can be used based on a free CAELinux-Live-DVD that is available in the Internet (works on most machines and operating systems). |
| the linear algebra survival guide illustrated with mathematica: Teaching Engineering, Second Edition Phillip C. Wankat, Frank S. Oreovicz, 2015-01-15 The majority of professors have never had a formal course in education, and the most common method for learning how to teach is on-the-job training. This represents a challenge for disciplines with ever more complex subject matter, and a lost opportunity when new active learning approaches to education are yielding dramatic improvements in student learning and retention. This book aims to cover all aspects of teaching engineering and other technical subjects. It presents both practical matters and educational theories in a format useful for both new and experienced teachers. It is organized to start with specific, practical teaching applications and then leads to psychological and educational theories. The practical orientation section explains how to develop objectives and then use them to enhance student learning, and the theoretical orientation section discusses the theoretical basis for learning/teaching and its impact on students. Written mainly for PhD students and professors in all areas of engineering, the book may be used as a text for graduate-level classes and professional workshops or by professionals who wish to read it on their own. Although the focus is engineering education, most of this book will be useful to teachers in other disciplines. Teaching is a complex human activity, so it is impossible to develop a formula that guarantees it will be excellent. However, the methods in this book will help all professors become good teachers while spending less time preparing for the classroom. This is a new edition of the well-received volume published by McGraw-Hill in 1993. It includes an entirely revised section on the Accreditation Board for Engineering and Technology (ABET) and new sections on the characteristics of great teachers, different active learning methods, the application of technology in the classroom (from clickers to intelligent tutorial systems), and how people learn. |
| the linear algebra survival guide illustrated with mathematica: MATRIX AND LINEAR ALGEBRA AIDED WITH MATLAB, Third Edition Kanti Bhushan Datta, 2016-12-01 With the inclusion of applications of singular value decomposition (SVD) and principal component analysis (PCA) to image compression and data analysis, this edition provides a strong foundation of linear algebra needed for a higher study in signal processing. The use of MATLAB in the study of linear algebra for a variety of computational purposes and the programmes provided in this text are the most attractive features of this book which strikingly distinguishes it from the existing linear algebra books needed as pre-requisites for the study of engineering subjects. This book is highly suitable for undergraduate as well as postgraduate students of mathematics, statistics, and all engineering disciplines. The book will also be useful to Ph.D. students for relevant mathematical resources. NEW TO THIS EDITION The Third Edition of this book includes: • Simultaneous diagonalization of two diagonalizable matrices • Comprehensive exposition of SVD with applications in shear analysis in engineering • Polar Decomposition of a matrix • Numerical experimentation with a colour and a black-and-white image compression using MATLAB • PCA methods of data analysis and image compression with a list of MATLAB codes |
| the linear algebra survival guide illustrated with mathematica: Persistence Theory: From Quiver Representations to Data Analysis Steve Y. Oudot, 2017-05-17 Persistence theory emerged in the early 2000s as a new theory in the area of applied and computational topology. This book provides a broad and modern view of the subject, including its algebraic, topological, and algorithmic aspects. It also elaborates on applications in data analysis. The level of detail of the exposition has been set so as to keep a survey style, while providing sufficient insights into the proofs so the reader can understand the mechanisms at work. The book is organized into three parts. The first part is dedicated to the foundations of persistence and emphasizes its connection to quiver representation theory. The second part focuses on its connection to applications through a few selected topics. The third part provides perspectives for both the theory and its applications. The book can be used as a text for a course on applied topology or data analysis. |
| the linear algebra survival guide illustrated with mathematica: The Best Books for Academic Libraries: Science, technology, and agriculture , 2002 |
| the linear algebra survival guide illustrated with mathematica: The Art and Craft of Problem Solving Paul Zeitz, 2016-11-14 Appealing to everyone from college-level majors to independent learners, The Art and Craft of Problem Solving, 3rd Edition introduces a problem-solving approach to mathematics, as opposed to the traditional exercises approach. The goal of The Art and Craft of Problem Solving is to develop strong problem solving skills, which it achieves by encouraging students to do math rather than just study it. Paul Zeitz draws upon his experience as a coach for the international mathematics Olympiad to give students an enhanced sense of mathematics and the ability to investigate and solve problems. |
| the linear algebra survival guide illustrated with mathematica: Generalized Linear Models P. McCullagh, 2019-01-22 The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and ot |
| the linear algebra survival guide illustrated with mathematica: An Introduction to Mathematical Modeling Edward A. Bender, 2012-05-23 Employing a practical, learn by doing approach, this first-rate text fosters the development of the skills beyond the pure mathematics needed to set up and manipulate mathematical models. The author draws on a diversity of fields — including science, engineering, and operations research — to provide over 100 reality-based examples. Students learn from the examples by applying mathematical methods to formulate, analyze, and criticize models. Extensive documentation, consisting of over 150 references, supplements the models, encouraging further research on models of particular interest. The lively and accessible text requires only minimal scientific background. Designed for senior college or beginning graduate-level students, it assumes only elementary calculus and basic probability theory for the first part, and ordinary differential equations and continuous probability for the second section. All problems require students to study and create models, encouraging their active participation rather than a mechanical approach. Beyond the classroom, this volume will prove interesting and rewarding to anyone concerned with the development of mathematical models or the application of modeling to problem solving in a wide array of applications. |
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Use Linear for free with your whole team. Upgrade to enable unlimited issues, enhanced security controls, and additional features.
Customer Requests – Linear
Capture feedback across any customer interaction – from sales calls to support chats – and turn it into a customer request linked to a Linear project or issue.
Linear MCP server – Changelog
May 1, 2025 · Your AI models and agents can use our official MCP server to access your Linear data in a simple and secure way. Connect to our MCP server natively as a new Claude …
About – Linear
Today, thousands of teams around the globe — from early-stage startups to public companies — use Linear to build their products. Linear helps them to focus on what they do best: Crafting …
Linear Method – Practices for building
The quality of a product is driven by both the talent of its creators and how they feel while they’re crafting it. To bring back the right focus, these are the foundational and evolving ideas Linear …
Features – Linear
With its meticulous design, breathtaking speed and purpose-built workflows, Linear unlocks your team’s full potential. It is the tool of choice for high-performance teams to build products better.
Linear Customers
Linear Projects give Mercury a source-of-truth across all their work. Read story. Watershed switched to Linear to accelerate their team. Read story. Linear keeps Retool's teams customer …
Building our way - Linear Blog
Jun 10, 2025 · Linear is unique because it actually manages all these workflows in a structured way and in a single product, bringing teams together. We see this end-to-end workflow as the …
Linear – Plan and build products
Linear is a purpose-built tool for modern product development. Streamline issues, projects, and product roadmaps.
Download Linear
Download the Linear app for desktop and mobile. Available for Mac, Windows, iOS, and Android.
Pricing – Linear
Use Linear for free with your whole team. Upgrade to enable unlimited issues, enhanced security controls, and additional features.
Customer Requests – Linear
Capture feedback across any customer interaction – from sales calls to support chats – and turn it into a customer request linked to a Linear project or issue.
Linear MCP server – Changelog
May 1, 2025 · Your AI models and agents can use our official MCP server to access your Linear data in a simple and secure way. Connect to our MCP server natively as a new Claude …
About – Linear
Today, thousands of teams around the globe — from early-stage startups to public companies — use Linear to build their products. Linear helps them to focus on what they do best: Crafting …
Linear Method – Practices for building
The quality of a product is driven by both the talent of its creators and how they feel while they’re crafting it. To bring back the right focus, these are the foundational and evolving ideas Linear …
Features – Linear
With its meticulous design, breathtaking speed and purpose-built workflows, Linear unlocks your team’s full potential. It is the tool of choice for high-performance teams to build products better.
Linear Customers
Linear Projects give Mercury a source-of-truth across all their work. Read story. Watershed switched to Linear to accelerate their team. Read story. Linear keeps Retool's teams customer …
Building our way - Linear Blog
Jun 10, 2025 · Linear is unique because it actually manages all these workflows in a structured way and in a single product, bringing teams together. We see this end-to-end workflow as the …
