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Learning Advanced Python by Studying Open Source Projects

Learning Advanced Python by Studying Open Source Projects

This book is one of its own kind. It is not an encyclopedia or a hands-on tutorial that traps readers in the tutorial hell. It is a distillation of just one common Python user’s learning experience. The experience is packaged with exceptional teaching techniques careful dependence unraveling and most importantly passion. Learning Advanced Python by Studying Open Source Projects helps readers overcome the difficulty in their day-to-day tasks and seek insights from solutions in famous open source projects. Different from a technical manual this book mixes the technical knowledge real-world applications and more theoretical content providing readers with a practical and engaging approach to learning Python. Throughout this book readers will learn how to write Python code that is efficient readable and maintainable covering key topics such as data structures algorithms object-oriented programming and more. The author’s passion for Python shines through in this book making it an enjoyable and inspiring read for both beginners and experienced programmers.

GBP 44.99
1

Introduction to Software Engineering

Introduction to Software Engineering

Practical Guidance on the Efficient Development of High-Quality SoftwareIntroduction to Software Engineering Second Edition equips students with the fundamentals to prepare them for satisfying careers as software engineers regardless of future changes in the field even if the changes are unpredictable or disruptive in nature. Retaining the same organization as its predecessor this second edition adds considerable material on open source and agile development models. The text helps students understand software development techniques and processes at a reasonably sophisticated level. Students acquire practical experience through team software projects. Throughout much of the book a relatively large project is used to teach about the requirements design and coding of software. In addition a continuing case study of an agile software development project offers a complete picture of how a successful agile project can work. The book covers each major phase of the software development life cycle from developing software requirements to software maintenance. It also discusses project management and explains how to read software engineering literature. Three appendices describe software patents command-line arguments and flowcharts.

GBP 44.99
1

Introduction to Mathematical Modeling and Computer Simulations

Introduction to Mathematical Modeling and Computer Simulations

Introduction to Mathematical Modeling and Computer Simulations is written as a textbook for readers who want to understand the main principles of Modeling and Simulations in settings that are important for the applications without using the profound mathematical tools required by most advanced texts. It can be particularly useful for applied mathematicians and engineers who are just beginning their careers. The goal of this book is to outline Mathematical Modeling using simple mathematical descriptions making it accessible for first- and second-year students. Chapter 1 and the Preface of this book is freely available as a downloadable Open Access PDF under a Creative Commons Attribution-Non Commercial-No Derivatives 4. 0 license available at http://www. taylorfrancis. com/books/e/9781315277240

GBP 44.99
1

Introduction to Self-Driving Vehicle Technology

Introduction to Self-Driving Vehicle Technology

This book aims to teach the core concepts that make Self-driving vehicles (SDVs) possible. It is aimed at people who want to get their teeth into self-driving vehicle technology by providing genuine technical insights where other books just skim the surface. The book tackles everything from sensors and perception to functional safety and cybersecurity. It also passes on some practical know-how and discusses concrete SDV applications along with a discussion of where this technology is heading. It will serve as a good starting point for software developers or professional engineers who are eager to pursue a career in this exciting field and want to learn more about the basics of SDV algorithms. Likewise academic researchers technology enthusiasts and journalists will also find the book useful. Key Features: Offers a comprehensive technological walk-through of what really matters in SDV development: from hardware software to functional safety and cybersecurity Written by an active practitioner with extensive experience in series development and research in the fields of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Covers theoretical fundamentals of state-of-the-art SLAM multi-sensor data fusion and other SDV algorithms. Includes practical information and hands-on material with Robot Operating System (ROS) and Open Source Car Control (OSCC). Provides an overview of the strategies trends and applications which companies are pursuing in this field at present as well as other technical insights from the industry. | Introduction to Self-Driving Vehicle Technology

GBP 48.99
1

Security Analytics A Data Centric Approach to Information Security

Security Analytics A Data Centric Approach to Information Security

The book gives a comprehensive overview of security issues in cyber physical systems by examining and analyzing the vulnerabilities. It also brings current understanding of common web vulnerabilities and its analysis while maintaining awareness and knowledge of contemporary standards practices procedures and methods of Open Web Application Security Project. This book is a medium to funnel creative energy and develop new skills of hacking and analysis of security and expedites the learning of the basics of investigating crimes including intrusion from the outside and damaging practices from the inside how criminals apply across devices networks and the internet at large and analysis of security data. Features Helps to develop an understanding of how to acquire prepare visualize security data. Unfolds the unventured sides of the cyber security analytics and helps spread awareness of the new technological boons. Focuses on the analysis of latest development challenges ways for detection and mitigation of attacks advanced technologies and methodologies in this area. Designs analytical models to help detect malicious behaviour. The book provides a complete view of data analytics to the readers which include cyber security issues analysis threats vulnerabilities novel ideas analysis of latest techniques and technology mitigation of threats and attacks along with demonstration of practical applications and is suitable for a wide-ranging audience from graduates to professionals/practitioners and researchers. | Security Analytics A Data Centric Approach to Information Security

GBP 150.00
1

An Introduction to Acceptance Sampling and SPC with R

An Introduction to Acceptance Sampling and SPC with R

An Introduction to Acceptance Sampling and SPC with R is an introduction to statistical methods used in monitoring controlling and improving quality. Topics covered include acceptance sampling; Shewhart control charts for Phase I studies; graphical and statistical tools for discovering and eliminating the cause of out-of-control-conditions; Cusum and EWMA control charts for Phase II process monitoring; and the design and analysis of experiments for process troubleshooting and discovering ways to improve process output. Origins of statistical quality control and the technical topics presented in the remainder of the book are those recommended in the ANSI/ASQ/ISO guidelines and standards for industry. The final chapter ties everything together by discussing modern management philosophies that encourage the use of the technical methods presented earlier. In the modern world sampling plans and the statistical calculations used in statistical quality control are done with the help of computers. As an open source high-level programming language with flexible graphical output options R runs on Windows Mac and Linux operating systems and has add-on packages that equal or exceed the capability of commercial software for statistical methods used in quality control. In this book we will focus on several R packages. In addition to demonstrating how to use R for acceptance sampling and control charts this book will concentrate on how the use of these specific tools can lead to quality improvements both within a company and within their supplier companies. This would be a suitable book for a one-semester undergraduate course emphasizing statistical quality control for engineering majors (such as manufacturing engineering or industrial engineering) or a supplemental text for a graduate engineering course that included quality control topics.

GBP 48.99
1

Introduction to Linear Algebra

Introduction to Linear Algebra

Linear algebra provides the essential mathematical tools to tackle all the problems in Science. Introduction to Linear Algebra is primarily aimed at students in applied fields (e. g. Computer Science and Engineering) providing them with a concrete rigorous approach to face and solve various types of problems for the applications of their interest. This book offers a straightforward introduction to linear algebra that requires a minimal mathematical background to read and engage with. Features Presented in a brief informative and engaging style Suitable for a wide broad range of undergraduates Contains many worked examples and exercises

GBP 44.99
1

Introduction to Number Theory

Introduction to Number Theory

Introduction to Number Theory covers the essential content of an introductory number theory course including divisibility and prime factorization congruences and quadratic reciprocity. The instructor may also choose from a collection of additional topics. Aligning with the trend toward smaller essential texts in mathematics the author strives for clarity of exposition. Proof techniques and proofs are presented slowly and clearly. The book employs a versatile approach to the use of algebraic ideas. Instructors who wish to put this material into a broader context may do so though the author introduces these concepts in a non-essential way. A final chapter discusses algebraic systems (like the Gaussian integers) presuming no previous exposure to abstract algebra. Studying general systems helps students to realize unique factorization into primes is a more subtle idea than may at first appear; students will find this chapter interesting fun and quite accessible. Applications of number theory include several sections on cryptography and other applications to further interest instructors and students alike.

GBP 38.99
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An Introduction to Analysis

An Introduction to Analysis

The third edition of this widely popular textbook is authored by a master teacher. This book provides a mathematically rigorous introduction to analysis of real­valued functions of one variable. This intuitive student-friendly text is written in a manner that will help to ease the transition from primarily computational to primarily theoretical mathematics. The material is presented clearly and as intuitive as possible while maintaining mathematical integrity. The author supplies the ideas of the proof and leaves the write-up as an exercise. The text also states why a step in a proof is the reasonable thing to do and which techniques are recurrent. Examples while no substitute for a proof are a valuable tool in helping to develop intuition and are an important feature of this text. Examples can also provide a vivid reminder that what one hopes might be true is not always true. Features of the Third Edition: Begins with a discussion of the axioms of the real number system. The limit is introduced via sequences. Examples motivate what is to come highlight the need for hypothesis in a theorem and make abstract ideas more concrete. A new section on the Cantor set and the Cantor function. Additional material on connectedness. Exercises range in difficulty from the routine getting your feet wet types of problems to the moderately challenging problems. Topology of the real number system is developed to obtain the familiar properties of continuous functions. Some exercises are devoted to the construction of counterexamples. The author presents the material to make the subject understandable and perhaps exciting to those who are beginning their study of abstract mathematics. Table of Contents Preface Introduction The Real Number System Sequences of Real Numbers Topology of the Real Numbers Continuous Functions Differentiation Integration Series of Real Numbers Sequences and Series of Functions Fourier Series Bibliography Hints and Answers to Selected Exercises Index Biography James R. Kirkwood holds a Ph. D. from University of Virginia. He has authored fifteen published mathematics textbooks on various topics including calculus real analysis mathematical biology and mathematical physics. His original research was in mathematical physics and he co-authored the seminal paper in a topic now called Kirkwood-Thomas Theory in mathematical physics. During the summer he teaches real analysis to entering graduate students at the University of Virginia. He has been awarded several National Science Foundation grants. His texts Elementary Linear Algebra Linear Algebra and Markov Processes are also published by CRC Press. | An Introduction to Analysis

GBP 82.99
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Introduction to Mathematical Modeling

Introduction to Mathematical Modeling

Introduction to Mathematical Modeling helps students master the processes used by scientists and engineers to model real-world problems including the challenges posed by space exploration climate change energy sustainability chaotic dynamical systems and random processes. Primarily intended for students with a working knowledge of calculus but minimal training in computer programming in a first course on modeling the more advanced topics in the book are also useful for advanced undergraduate and graduate students seeking to get to grips with the analytical numerical and visual aspects of mathematical modeling as well as the approximations and abstractions needed for the creation of a viable model.

GBP 84.99
1

Introduction to Biological Networks

Introduction to Biological Networks

The new research area of genomics-inspired network biology lacks an introductory book that enables both physical/computational scientists and biologists to obtain a general yet sufficiently rigorous perspective of current thinking. Filling this gap Introduction to Biological Networks provides a thorough introduction to genomics-inspired network biology for physical scientists and biologists involved in interdisciplinary research. The book focuses on the concept of molecular and genetic interaction networks as a paradigm for interpreting the complexity of molecular biology at a genomic scale. The authors describe the experimental methods used to discover and test networks of interaction among biological molecules. They also present computational methods for predicting the interaction networks discuss general mechanisms of network formation and evolution and explore the application of network approaches to important problems in biology and medicine. With many examples throughout and clear explanations of key concepts this book is the first to offer a broad treatment of genomics-inspired network biology with sufficient mathematical and biological rigor. It gives readers a conceptual understanding of this burgeoning scientific field.

GBP 59.99
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Introduction to Real Analysis

Introduction to Real Analysis

This classic textbook has been used successfully by instructors and students for nearly three decades. This timely new edition offers minimal yet notable changes while retaining all the elements presentation and accessible exposition of previous editions. A list of updates is found in the Preface to this edition. This text is based on the author’s experience in teaching graduate courses and the minimal requirements for successful graduate study. The text is understandable to the typical student enrolled in the course taking into consideration the variations in abilities background and motivation. Chapters one through six have been written to be accessible to the average student w hile at the same time challenging the more talented student through the exercises. Chapters seven through ten assume the students have achieved some level of expertise in the subject. In these chapters the theorems examples and exercises require greater sophistication and mathematical maturity for full understanding. In addition to the standard topics the text includes topics that are not always included in comparable texts. Chapter 6 contains a section on the Riemann-Stieltjes integral and a proof of Lebesgue’s t heorem providing necessary and sufficient conditions for Riemann integrability. Chapter 7 also includes a section on square summable sequences and a brief introduction to normed linear spaces. C hapter 8 contains a proof of the Weierstrass approximation theorem using the method of aapproximate identities. The inclusion of Fourier series in the text allows the student to gain some exposure to this important subject. The final chapter includes a detailed treatment of Lebesgue measure and the Lebesgue integral using inner and outer measure. The exercises at the end of each section reinforce the concepts. Notes provide historical comments or discuss additional topics. | Introduction to Real Analysis

GBP 46.99
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Field Guide to Compelling Analytics

Field Guide to Compelling Analytics

Field Guide to Compelling Analytics is written for Analytics Professionals (APs) who want to increase their probability of success in implementing analytical solutions. In the past soft skills such as presentation and persuasive writing techniques have been the extent of teaching junior APs how to effectively communicate the value of analytical products. However there are other aspects to success such as trust and experience that may play a more important role in convincing fellow APs clients advisors and leadership groups that their analytic solutions will work. This book introduces the formula ‘Analytics + Trust + Communication + Experience > Convince Them’ to illustrate an AP’s ability to convince a stakeholder. The ‘Convince Me’ stakeholders might be an analytics team member team lead decision-maker or senior leader that are either internal or external to the AP’s organization. Whoever they are this formula represents a concise digestible and above all practical means to increase the likelihood that you will be able to persuade them of the value of your analytical product. Features Includes insight questions to support class discussion Written in broadly non-mathematical terms designed to be accessible to any level of student or practicing AP to read understand and implement the concepts Each section introduces the ideas through real-life case studies

GBP 48.99
1

Introduction to Python Programming

Introduction to Python Programming

Introduction to Python Programming is written for students who are beginners in the field of computer programming. This book presents an intuitive approach to the concepts of Python Programming for students. This book differs from traditional texts not only in its philosophy but also in its overall focus level of activities development of topics and attention to programming details. The contents of the book are chosen with utmost care after analyzing the syllabus for Python course prescribed by various top universities in USA Europe and Asia. Since the prerequisite know-how varies significantly from student to student the book’s overall overture addresses the challenges of teaching and learning of students which is fine-tuned by the authors’ experience with large sections of students. This book uses natural language expressions instead of the traditional shortened words of the programming world. This book has been written with the goal to provide students with a textbook that can be easily understood and to make a connection between what students are learning and how they may apply that knowledge. Features of this book This book does not assume any previous programming experience although of course any exposure to other programming languages is useful This book introduces all of the key concepts of Python programming language with helpful illustrations Programming examples are presented in a clear and consistent manner Each line of code is numbered and explained in detail Use of f-strings throughout the book Hundreds of real-world examples are included and they come from fields such as entertainment sports music and environmental studies Students can periodically check their progress with in-chapter quizzes that appear in all chapters

GBP 160.00
1

Introduction to Probability Second Edition

Introduction to Probability Second Edition

Developed from celebrated Harvard statistics lectures Introduction to Probability provides essential language and tools for understanding statistics randomness and uncertainty. The book explores a wide variety of applications and examples ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics medicine computer science and information theory. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations diagrams and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R a free statistical software environment. The second edition adds many new examples exercises and explanations to deepen understanding of the ideas clarify subtle concepts and respond to feedback from many students and readers. New supplementary online resources have been developed including animations and interactive visualizations and the book has been updated to dovetail with these resources. Supplementary material is available on Joseph Blitzstein’s website www. stat110. net. The supplements include:Solutions to selected exercisesAdditional practice problemsHandouts including review material and sample exams Animations and interactive visualizations created in connection with the edX online version of Stat 110. Links to lecture videos available on ITunes U and YouTube There is also a complete instructor's solutions manual available to instructors who require the book for a course. | Introduction to Probability Second Edition

GBP 66.99
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Introduction to Math Olympiad Problems

Introduction to Math Olympiad Problems

Introduction to Math Olympiad Problems aims to introduce high school students to all the necessary topics that frequently emerge in international Math Olympiad competitions. In addition to introducing the topics the book will also provide several repetitive-type guided problems to help develop vital techniques in solving problems correctly and efficiently. The techniques employed in the book will help prepare students for the topics they will typically face in an Olympiad-style event but also for future college mathematics courses in Discrete Mathematics Graph Theory Differential Equations Number Theory and Abstract Algebra. Features: Numerous problems designed to embed good practice in readers and build underlying reasoning analysis and problem-solving skills Suitable for advanced high school students preparing for Math Olympiad competitions

GBP 24.99
1

Introduction to Probability with Mathematica

Introduction to Probability with Mathematica

Updated to conform to Mathematica® 7. 0 Introduction to Probability with Mathematica® Second Edition continues to show students how to easily create simulations from templates and solve problems using Mathematica. It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems. The accompanyingdownloadable resources offer instructors the option of creating class notes demonstrations and projects. New to the Second EditionExpanded section on Markov chains that includes a study of absorbing chainsNew sections on order statistics transformations of multivariate normal random variables and Brownian motionMore example data of the normal distribution More attention on conditional expectation which has become significant in financial mathematicsAdditional problems from Actuarial Exam PNew appendix that gives a basic introduction to MathematicaNew examples exercises and data sets particularly on the bivariate normal distributionNew visualization and animation features from Mathematica 7. 0Updated Mathematica notebooks on the downloadable resources. After covering topics in discrete probability the text presents a fairly standard treatment of common discrete distributions. It then transitions to continuous probability and continuous distributions including normal bivariate normal gamma and chi-square distributions. The author goes on to examine the history of probability the laws of large numbers and the central limit theorem. The final chapter explores stochastic processes and applications ideal for students in operations research and finance.

GBP 59.99
1

Practical Guide to Logistic Regression

Practical Guide to Logistic Regression

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields including medical and health outcomes research business analytics and data science ecology fisheries astronomy transportation insurance economics recreation and sports. By harnessing the capabilities of the logistic model analysts can better understand their data make appropriate predictions and classifications and determine the odds of one value of a predictor compared to another. Drawing on his many years of teaching logistic regression using logistic-based models in research and writing about the subject Professor Hilbe focuses on the most important features of the logistic model. Serving as a guide between the author and readers the book explains how to construct a logistic model interpret coefficients and odds ratios predict probabilities and their standard errors based on the model and evaluate the model as to its fit. Using a variety of real data examples mostly from health outcomes the author offers a basic step-by-step guide to developing and interpreting observation and grouped logistic models as well as penalized and exact logistic regression. He also gives a step-by-step guide to modeling Bayesian logistic regression. R statistical software is used throughout the book to display the statistical models while SAS and Stata codes for all examples are included at the end of each chapter. The example code can be adapted to readers own analyses. All the code is available on the author‘s website.

GBP 175.00
1

Introduction to Python for Humanists

Introduction to Python for Humanists

This book will introduce digital humanists at all levels of education to Python. It provides background and guidance on learning the Python computer programming language and as it presumes no knowledge on the part of the reader about computers or coding concepts allows the reader to gradually learn the more complex tasks that are currently popular in the field of digital humanities. This book will be aimed at undergraduates graduates and faculty who are interested in learning how to use Python as a tool within their workflow. An Introduction to Python for Digital Humanists will act as a primer for students who wish to use Python allowing them to engage with more advanced textbooks. This book fills a real need as it is first Python introduction to be aimed squarely at humanities students as other books currently available do not approach Python from a humanities perspective. It will be designed so that those experienced in Python can teach from it in addition to allowing those who are interested in being self-taught can use it for that purpose. Key Features: Data analysis Data science Computational humanities Digital humanities Python Natural language processing Social network analysis App development | Introduction to Python for Humanists

GBP 44.99
1

Transition to Advanced Mathematics

Transition to Advanced Mathematics

This unique and contemporary text not only offers an introduction to proofs with a view towards algebra and analysis a standard fare for a transition course but also presents practical skills for upper-level mathematics coursework and exposes undergraduate students to the context and culture of contemporary mathematics. The authors implement the practice recommended by the Committee on the Undergraduate Program in Mathematics (CUPM) curriculum guide that a modern mathematics program should include cognitive goals and offer a broad perspective of the discipline. Part I offers: An introduction to logic and set theory. Proof methods as a vehicle leading to topics useful for analysis topology algebra and probability. Many illustrated examples often drawing on what students already know that minimize conversation about doing proofs. An appendix that provides an annotated rubric with feedback codes for assessing proof writing. Part II presents the context and culture aspects of the transition experience including: 21st century mathematics including the current mathematical culture vocations and careers. History and philosophical issues in mathematics. Approaching reading and learning from journal articles and other primary sources. Mathematical writing and typesetting in LaTeX. Together these Parts provide a complete introduction to modern mathematics both in content and practice. Table of Contents Part I - Introduction to Proofs Logic and Sets Arguments and Proofs Functions Properties of the Integers Counting and Combinatorial Arguments RelationsPart II - Culture History Reading and Writing Mathematical Culture Vocation and Careers History and Philosophy of Mathematics Reading and Researching Mathematics Writing and Presenting Mathematics Appendix A. Rubric for Assessing Proofs Appendix B. Index of Theorems and Definitions from Calculus and Linear Algebra Bibliography Index Biographies Danilo R. Diedrichs is an Associate Professor of Mathematics at Wheaton College in Illinois. Raised and educated in Switzerland he holds a PhD in applied mathematical and computational sciences from the University of Iowa as well as a master’s degree in civil engineering from the Ecole Polytechnique Fédérale in Lausanne Switzerland. His research interests are in dynamical systems modeling applied to biology ecology and epidemiology. Stephen Lovett is a Professor of Mathematics at Wheaton College in Illinois. He holds a PhD in representation theory from Northeastern University. His other books include Abstract Algebra: Structures and Applications (2015) Differential Geometry of Curves and Surfaces with Tom Banchoff (2016) and Differential Geometry of Manifolds (2019). | Transition to Advanced Mathematics

GBP 82.99
1

A Bridge to Higher Mathematics

A Bridge to Higher Mathematics

A Bridge to Higher Mathematics is more than simply another book to aid the transition to advanced mathematics. The authors intend to assist students in developing a deeper understanding of mathematics and mathematical thought. The only way to understand mathematics is by doing mathematics. The reader will learn the language of axioms and theorems and will write convincing and cogent proofs using quantifiers. Students will solve many puzzles and encounter some mysteries and challenging problems. The emphasis is on proof. To progress towards mathematical maturity it is necessary to be trained in two aspects: the ability to read and understand a proof and the ability to write a proof. The journey begins with elements of logic and techniques of proof then with elementary set theory relations and functions. Peano axioms for positive integers and for natural numbers follow in particular mathematical and other forms of induction. Next is the construction of integers including some elementary number theory. The notions of finite and infinite sets cardinality of counting techniques and combinatorics illustrate more techniques of proof. For more advanced readers the text concludes with sets of rational numbers the set of reals and the set of complex numbers. Topics like Zorn‘s lemma and the axiom of choice are included. More challenging problems are marked with a star. All these materials are optional depending on the instructor and the goals of the course.

GBP 175.00
1

Introduction to Mathematical Oncology

Introduction to Mathematical Oncology

Introduction to Mathematical Oncology presents biologically well-motivated and mathematically tractable models that facilitate both a deep understanding of cancer biology and better cancer treatment designs. It covers the medical and biological background of the diseases modeling issues and existing methods and their limitations. The authors introduce mathematical and programming tools along with analytical and numerical studies of the models. They also develop new mathematical tools and look to future improvements on dynamical models. After introducing the general theory of medicine and exploring how mathematics can be essential in its understanding the text describes well-known practical and insightful mathematical models of avascular tumor growth and mathematically tractable treatment models based on ordinary differential equations. It continues the topic of avascular tumor growth in the context of partial differential equation models by incorporating the spatial structure and physiological structure such as cell size. The book then focuses on the recent active multi-scale modeling efforts on prostate cancer growth and treatment dynamics. It also examines more mechanistically formulated models including cell quota-based population growth models with applications to real tumors and validation using clinical data. The remainder of the text presents abundant additional historical biological and medical background materials for advanced and specific treatment modeling efforts. Extensively classroom-tested in undergraduate and graduate courses this self-contained book allows instructors to emphasize specific topics relevant to clinical cancer biology and treatment. It can be used in a variety of ways including a single-semester undergraduate course a more ambitious graduate course or a full-year sequence on mathematical oncology.

GBP 44.99
1

An Introduction to Metric Spaces

An Introduction to Metric Spaces

This book serves as a textbook for an introductory course in metric spaces for undergraduate or graduate students. The goal is to present the basics of metric spaces in a natural and intuitive way and encourage students to think geometrically while actively participating in the learning of this subject. In this book the authors illustrated the strategy of the proofs of various theorems that motivate readers to complete them on their own. Bits of pertinent history are infused in the text including brief biographies of some of the central players in the development of metric spaces. The textbook is divided into seven chapters that contain the main materials on metric spaces; namely introductory concepts completeness compactness connectedness continuous functions and metric fixed point theorems with applications. Some of the noteworthy features of this book include · Diagrammatic illustrations that encourage readers to think geometrically · Focus on systematic strategy to generate ideas for the proofs of theorems · A wealth of remarks observations along with a variety of exercises · Historical notes and brief biographies appearing throughout the text | An Introduction to Metric Spaces

GBP 32.99
1

Introduction to Computational Proteomics

Introduction to Computational Proteomics

Introduction to Computational Proteomics introduces the field of computational biology through a focused approach that tackles the different steps and problems involved with protein analysis classification and meta-organization. The book starts with the analysis of individual entities and works its way through the analysis of more complex entities from protein families to interactions cellular pathways and gene networks. The first part of the book presents methods for identifying the building blocks of the protein space such as motifs and domains. It also describes algorithms for assessing similarity between proteins based on sequence and structure analysis as well as mathematical models such as hidden Markov models and support vector machines that are used to represent protein families and classify new instances. The second part covers methods that investigate higher order structure in the protein space through the application of unsupervised learning algorithms such as clustering and embedding. The book also explores the broader context of proteins. It discusses methods for analyzing gene expression data predicting protein-protein interactions elucidating cellular pathways and reconstructing gene networks. This book provides a coherent and thorough introduction to proteome analysis. It offers rigorous formal descriptions along with detailed algorithmic solutions and models. Each chapter includes problem sets from courses taught by the author at Cornell University and the Technion. Software downloads data sets and other material are available at biozon. org

GBP 59.99
1