{"title":"Maths for computer scientists Books","description":"","products":[{"product_id":"modern-fortran-explained-9780198876588","title":"Modern Fortran Explained","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis new edition of Modern Fortran Explained provides a clear and thorough description of the latest version of Fortran, written by experts in the field with the intention that it remain the main reference work in the field.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1: Whence Fortran? 2: Language elements 3: Expressions and assignments 4: Control constructs 5: Program units and procedures 6: Allocation of data 7: Array features 8: Specification statements 9: Intrinsic procedures and modules 10: Data transfer 11: Edit descriptors 12: Operations on external files 13: Further type parameter featur 14: Abstract interfaces and procedure pointers 15: Object-oriented programming 16: Submodules 17: Coarrays 18: Coarray teams 19: Floating-point exception handling 20: Basic interoperability with C 21: Interoperating with C using descriptors 22: Generic programming 23: Other Fortran 2023 enhancements A: Deprecated features B: Obsolescent and deleted features C: Significant examples D: Solutions to exercises","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732826075479,"sku":"9780198876588","price":42.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198876588.jpg?v=1719998567"},{"product_id":"beginning-r-4-9781484260524","title":"Beginning R 4","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eLearn how to use R 4, write and save R scripts, read in and write out data files, use built-in functions, and understand common statistical methods. This in-depth tutorial includes key R 4 features including a new color palette for charts, an enhanced reference counting system (useful for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data). \u003c\/p\u003e\u003cp\u003eEach chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eBeginning R 4\u003c\/i\u003e shows the use of R in specific cases such as ANO\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1: Installing R2: Installing Packages and Using Libraries3: Data Input and Output4: Working with Data5: Data and Samples6: Descriptive Statistics7: Understanding Probability and Distribution8: Correlation and Regression9: Confidence Intervals10: Hypothesis Testing11: Multiple Regression12: Moderated Regression13: Analysts of VarianceBibliography\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739665903959,"sku":"9781484260524","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"a-course-in-mathematical-statistics-and-large-sample-theory-9781493940301","title":"A Course in Mathematical Statistics and Large","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cdiv\u003eThis graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics.\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003ePart I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“It deals with advanced statistical theory with a special focus on statistical inference and large sample theory, aiming to cover the material for a modern two-semester graduate course in mathematical statistics. … Overall, the book is very advanced and is recommended to graduate students with sound statistical backgrounds, as well as to teachers, researchers, and practitioners who wish to acquire more knowledge on mathematical statistics and large sample theory.” (Lefteris Angelis, Computing Reviews, March, 2017)\u003cp\u003e\u003c\/p\u003e\u003cp\u003e“This is a very nice book suitable for a theoretical statistics course after having worked through something at the level of Casella \u0026amp; Berger, as well as some measure theory. … In addition to the exercises, which range from doable to interesting, there are several projects scattered throughout the text. The explanations are clear and crisp, and the presentation is interesting. … the book would be a worthy addition to your statistics library.” (Peter Rabinovitch, MAA Reviews, maa.org, March, 2017)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Introduction.- 2 Decision Theory.- 3 Introduction to General Methods of Estimation.- 4 Sufficient Statistics, Exponential Families, and Estimation.- 5 Testing Hypotheses.- 6 Consistency and Asymptotic Distributions and Statistics.- 7 Large Sample Theory of Estimation in Parametric Models.- 8 Tests in Parametric and Nonparametric Models.- 9 The Nonparametric Bootstrap.- 10 Nonparametric Curve Estimation.- 11 Edgeworth Expansions and the Bootstrap.- 12 Frechet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces.- 13 Multiple Testing and the False Discovery Rate.- 14 Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory.- 15 Miscellaneous Topics.- Appendices.- Solutions of Selected Exercises in Part 1.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48739724853591,"sku":"9781493940301","price":82.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781493940301.jpg?v=1720053001"},{"product_id":"math-and-architectures-of-deep-learning-9781617296482","title":"Math and Architectures of Deep Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function.    \u003cb\u003e\u003ci\u003eMath and Architectures of Deep Learning\u003c\/i\u003e \u003c\/b\u003ebridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you'll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.      about the technology  It's important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems.    about the book    \u003cb\u003e\u003ci\u003eMath and Architectures of Deep Learning\u003c\/i\u003e \u003c\/b\u003esets out the foundations of DL in a way that's both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you're done, you'll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.    \u003cbr\u003e    \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This is a book that will reward your patience and perseverance with a clear and detailed knowledge of deep learning mathematics and associated techniques.'  \u003cb\u003eTony Holdroyd\u003c\/b\u003e    'Most online machine learning courses teach you how to get stuff done, but they don't give you the underlying math. If you want to know, this is the book for you!'  \u003cb\u003eWiebe de Jong\u003c\/b\u003e    'A really interesting book for people that want to understand the underlying mathematical mechanism of deep learning.'  \u003cb\u003eJulien Pohie\u003c\/b\u003e    'Gives a unique perspective about machine learning and mathematical approaches.'  \u003cb\u003eKrzysztof Kamyczek\u003c\/b\u003e    'An awesome book to get the grasp of the important mathematical skills to understand the very basics of deep learning.'  \u003cb\u003eNicole Koenigstein\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003etable of contents      READ IN LIVEBOOK  1AN OVERVIEW OF MACHINE LEARNING AND DEEP LEARNING      READ IN LIVEBOOK  2INTRODUCTION TO VECTORS, MATRICES AND TENSORS FROM MACHINE LEARNING AND DATA SCIENCE POINT OF VIEW      READ IN LIVEBOOK  3INTRODUCTION TO VECTOR CALCULUS FROM MACHINE LEARNING POINT OF VIEW      READ IN LIVEBOOK  4LINEAR ALGEBRAIC TOOLS IN MACHINE LEARNING AND DATA SCIENCE      READ IN LIVEBOOK  5PROBABILITY DISTRIBUTIONS FOR MACHINE LEARNING AND DATA SCIENCE      READ IN LIVEBOOK  6BAYESIAN TOOLS FOR MACHINE LEARNING AND DATA SCIENCE      READ IN LIVEBOOK  7FUNCTION APPROXIMATION: HOW NEURAL NETWORKS MODEL THE WORLD      READ IN LIVEBOOK  8TRAINING NEURAL NETWORKS: FORWARD AND BACKPROPAGATION      READ IN LIVEBOOK  9LOSS, OPTIMIZATION AND REGULARIZATION      READ IN LIVEBOOK  10ONE, TWO AND THREE DIMENSIONAL CONVOLUTION AND TRANSPOSED CONVOLUTION IN NEURAL NETWORKS      11 IMAGE ANALYSIS: 2D CONVOLUTION BASED NEURAL NETWORK ARCHITECTURES FOR OBJECT RECOGNITION AND DETECTION      12 VIDEO ANALYSIS: 3D CONVOLUTION BASED SPATIO TEMPORAL NEURAL NETWORK ARCHITECTURES      READ IN LIVEBOOK  APPENDIX A: APPENDIX      A.1Dot Product and cosine of the angle between two vectors      A.2Computing variance of Gaussian Distribution      A.3Two Theorems in Statistic","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48740644749655,"sku":"9781617296482","price":47.47,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617296482.jpg?v=1720055230"},{"product_id":"graph-algorithms-for-data-science-9781617299469","title":"Graph Algorithms for Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eGraphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment.\u003c\/b\u003e   \u003cbr\u003e   \u003cbr\u003eIn       \u003ci\u003eGraph Algorithms for Data Science\u003c\/i\u003e    you will learn:   \u003cbr\u003e   \u003cbr\u003e   \u003cul\u003e\n\u003cli\u003eLabeled-property graph modeling\u003c\/li\u003e\n\u003cli\u003eConstructing a graph from structured data such as CSV or SQL\u003c\/li\u003e\n\u003cli\u003eNLP techniques to construct a graph from unstructured data\u003c\/li\u003e\n\u003cli\u003eCypher query language syntax to manipulate data and extract insights\u003c\/li\u003e\n\u003cli\u003eSocial network analysis algorithms like PageRank and community detection\u003c\/li\u003e\n\u003cli\u003eHow to translate graph structure to a ML model input with node embedding models\u003c\/li\u003e\n\u003cli\u003eUsing graph features in node classification and link prediction workflows\u003c\/li\u003e\n\u003c\/ul\u003e   \u003cbr\u003e   \u003ci\u003eGraph Algorithms for Data Science\u003c\/i\u003e    is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.      about the technology  Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations.    about the book    \u003ci\u003eGraph Algorithms for Data Science\u003c\/i\u003e   teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection\/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e'The book covers topics in-depth but is easy to understand. Though delving into theory, it doesn't lose its focus of being a more practical guide. ' Carl Yu\u003c\/p\u003e \u003cp\u003e'A good starting point to getting started with network analysis and how to extract the essential information you need easily.' Andrea Paciolla\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e'A great introduction to how to use graphs and data they can provide.' Marcin Sęk\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003etable of contents  \u003ci\u003e\u003c\/i\u003e detailed TOC        READ IN LIVEBOOK  1GRAPHS AND NETWORK SCIENCE: AN INTRODUCTION      READ IN LIVEBOOK  2REPRESENTING NETWORK STRUCTURE - DESIGN YOUR FIRST GRAPH MODEL      READ IN LIVEBOOK  3YOUR FIRST STEPS WITH THE CYPHER QUERY LANGUAGE      READ IN LIVEBOOK  4CYPHER AGGREGATIONS AND SOCIAL NETWORK ANALYSIS      5 INFERRING NETWORKS AND MONOPARTITE PROJECTIONS      6 CONSTRUCT A GRAPH USING NLP TECHNIQUES      7 NODE EMBEDDINGS AND CLASSIFICATION      8 IMPROVE DOCUMENT CLASSIFICATION WITH GRAPH NEURAL NETWORKS      9 PREDICT NEW CONNECTIONS      10 KNOWLEDGE GRAPH COMPLETION      READ IN LIVEBOOK  APPENDIX A: ADJACENCY MATRIX","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48740646617431,"sku":"9781617299469","price":41.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617299469.jpg?v=1720055235"},{"product_id":"graph-drawing-and-network-visualization-27th-international-symposium-gd-2019-prague-czech-republic-september-17-20-2019-proceedings-9783030358013","title":"Graph Drawing and Network Visualization: 27th","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the refereed proceedings of the 27th International Symposium on Graph Drawing and Network Visualization, GD 2019, held in Prague, Czech Republic, in September 2019.\u003cp\u003eThe 42 papers and 12 posters presented in this volume were carefully reviewed and selected from 113 submissions. They were organized into the following topical sections: Cartograms and Intersection Graphs, Geometric Graph Theory, Clustering, Quality Metrics, Arrangements, A Low Number of Crossings, Best Paper in Track 1, Morphing and Planarity, Parameterized Complexity, Collinearities, Topological Graph Theory, Best Paper in Track 2, Level Planarity, Graph Drawing Contest Report, and Poster Abstracts.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eCartograms and Intersection Graphs.- \u003c\/b\u003eStick Graphs with Length Constraints.- Representing Graphs and Hypergraphs by Touching Polygons in 3D.- Optimal Morphs of Planar Orthogonal Drawings II.- Computing Stable Demers Cartograms.- \u003cb\u003eGeometric Graph Theory.-\u003c\/b\u003e Bundled Crossings Revisited.- Crossing Numbers of Beyond-Planar Graphs.- On the 2-Colored Crossing Number.- Minimal Representations of Order Types by Geometric Graphs.- Balanced Schnyder woods for planar triangulations: an experimental study with applications to graph drawing and graph separators.- \u003cb\u003eClustering.- \u003c\/b\u003eA Quality Metric for Visualization of Clusters in Graphs.-\u003cb\u003e \u003c\/b\u003eMulti-level Graph Drawing using Infomap Clustering.- On Strict (Outer-)Confluent Graphs.- \u003cb\u003eQuality Metrics.- \u003c\/b\u003eOn the Edge-Length Ratio of Planar Graphs.- Node Overlap Removal Algorithms: A Comparative Study.- Graphs with large total angular resolution.- \u003cb\u003eArrangements.- \u003c\/b\u003eComputing Height-Optimal Tangles Faster.-\u003cb\u003e \u003c\/b\u003eOn Arrangements of Orthogonal Circles.- Extending Simple Drawings.- Coloring Hasse diagrams and disjointness graphs of curves.- \u003cb\u003eA Low Number of Crossings.-\u003c\/b\u003e Efficient Generation of Different Topological Representations of Graphs Beyond-Planarity.- The QuaSEFE Problem.- ChordLink: A New Hybrid Visualization Model.- Stress-Plus-X (SPX) Graph Layout.- \u003cb\u003eBest Paper in Track 1.-\u003c\/b\u003e Exact Crossing Number Parameterized by Vertex Cover.- \u003cb\u003eMorphing and Planarity.-\u003c\/b\u003e Maximizing Ink in Partial Edge Drawings of k-Plane Graphs.- Graph Drawing with Morphing Partial Edges.- A Note on Universal Point Sets for Planar Graphs.- \u003cb\u003eParameterized Complexity.-\u003c\/b\u003e Parameterized Algorithms for Book Embedding Problems.- Sketched Representations and Orthogonal Planarity of Bounded Treewidth Graphs.- \u003cb\u003eCollinearities.- \u003c\/b\u003e4-Connected Triangulations on Few Lines.-\u003cb\u003e \u003c\/b\u003eLine and Plane Cover Numbers Revisited.-\u003cb\u003e \u003c\/b\u003eDrawing planar graphs with few segments on a polynomial grid.- Variants of the Segment Number of a Graph.-\u003cb\u003e Topological Graph Theory.- \u003c\/b\u003eLocal and Union Page Numbers.- Mixed Linear Layouts: Complexity, Heuristics, and Experiments.-\u003cb\u003e \u003c\/b\u003eHomotopy height, grid-major height and graph-drawing height.-\u003cb\u003e \u003c\/b\u003eOn the Edge-Vertex Ratio of Maximal Thrackles.- \u003cb\u003eBest Paper in Track 2.-\u003c\/b\u003e Symmetry Detection and Classification in Drawings of Graphs.- \u003cb\u003eLevel Planarity.-\u003c\/b\u003e An SPQR-Tree-Like Embedding Representation for Upward Planarity.- A Natural Quadratic Approach to the Generalized Graph Layering Problem.- Graph Stories in Small Area.- Level-Planar Drawings with Few Slopes.- \u003cb\u003eGraph Drawing Contest Report.- \u003c\/b\u003eGraph Drawing Contest Report.-\u003cb\u003e Poster Abstracts.- \u003c\/b\u003eA 1-planarity Testing and Embedding Algorithm.-\u003cb\u003e \u003c\/b\u003eStretching Two Pseudolines in Planar Straight-Line Drawings.-\u003cb\u003e \u003c\/b\u003eAdventures in Abstraction: Reachability in Hierarchical Drawings.-\u003cb\u003e \u003c\/b\u003eOn Topological Book Embedding for k-Plane Graphs.- On Compact RAC Drawings.- FPQ-choosable Planarity Testing.- Packing Trees into 1-Planar Graphs.- Geographic Network Visualization Techniques: A Work-In-Progress Taxonomy.- On the Simple Quasi Crossing Number of K 11.- Minimising Crossings in a Tree-Based Network.- Crossing Families and Their Generalizations.- Which Sets of Strings are Pseudospherical?.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743033930071,"sku":"9783030358013","price":44.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"audit-analytics-data-science-for-the-accounting-profession-9783030490904","title":"Audit Analytics: Data Science for the Accounting","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eToday, information technology plays a pivotal role in financial control and audit: most financial data is now digitally recorded and dispersed among servers, clouds and networks over which the audited firm has no control. Additionally, a firm’s data—particularly in the case of finance, software, insurance and biotech firms— comprises most of the audited value of the firm. Financial audits are critical mechanisms for ensuring the integrity of information systems and the reporting of organizational finances. They help avoid the abuses that led to passage of legislation such as the Foreign Corrupt Practices Act (1977), and the Sarbanes-Oxley Act (2002).\u003c\/p\u003e  \u003cp\u003eAudit effectiveness has declined over the past two decades as auditor skillsets have failed to keep up with advances in information technology. Information and communication technology lie at the core of commerce today and are integrated in business processes around the world. This book is designed to meet the increasing need of audit professionals to understand information technology and the controls required to manage it. The material included focuses on the requirements for annual Securities and Exchange Commission audits (10-K) for listed corporations. These represent the benchmark auditing procedures for specialized audits, such as internal, governmental, and attestation audits.\u003c\/p\u003e\u003cp\u003eUsing R and RStudio, the book demonstrates how to render an audit opinion that is legally and statistically defensible; analyze, extract, and manipulate accounting data; build a risk assessment matrix to inform the conduct of a cost-effective audit program; and more.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Fundamentals of Auditing Financial Statements.- 2. Foundations of Audit Analytics.- 3. Analysis of Accounting Transactions.- 4. Risk Assessment and Planning.- 5. Analytical Review: Technical Analysis.- 6. Analytical Review: Intelligence Scanning.- 7. Design of Audit Programs.- 8. Interim Compliance Tests.- 9. Substantive Tests.- 10. Sarbanes-Oxley Engagements.- 11. Blockchains, Cybercrime and Forensics.- 12. Special Engagements: Forecasts and Valuation.- 13. Simulated Transactions for Auditing Service Organizations.\u003cbr\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743037665623,"sku":"9783030490904","price":59.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"understand-mathematics-understand-computing-discrete-mathematics-that-all-computing-students-should-know-9783030583750","title":"Understand Mathematics, Understand Computing:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIn this book the authors aim to endow the reader with an operational, conceptual, and methodological understanding of the discrete mathematics that can be used to study, understand, and perform computing. They want the reader to understand the elements of computing, rather than just know them. The basic topics are presented in a way that encourages readers to develop their personal way of thinking about mathematics. Many topics are developed at several levels, in a single voice, with sample applications from within the world of computing. Extensive historical and cultural asides emphasize the human side of mathematics and mathematicians.\u003cbr\u003eBy means of lessons and exercises on “doing” mathematics, the book prepares interested readers to develop new concepts and invent new techniques and technologies that will enhance all aspects of computing. The book will be of value to students, scientists, and engineers engaged in the design and use of computing systems, and to scholars and practitioners beyond these technical fields who want to learn and apply novel computational ideas.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The text is written in an easy to read format which generously incorporates narratives from the history of mathematics as well as rigorous proofs of the concepts presented. The appendices and references to other texts provide the reader with numerous sources of supplementary information for those wishing to delve into a subject at a deeper level … . chapters are organized and clearly labeled to express which sections are appropriate for a beginning learner, an intermediate learner, or the specialist.” (Tom French, MAA Reviews, October 3, 2021)\u003cbr\u003e\u003cbr\u003e“Each chapter comes with several exercises from easy to difficult, the latter with complete solutions in the appendix. To accommodate the book to readers with different backgrounds and goals, the authors provide a guide which gives paths through the book for several courses. The exposition is always clear and motivating, no prerequisites are presumed, all terms and concepts are defined precisely, and there are many look-and-see proofs.” (Dieter Riebesehl, zbMATH 1465.68004, 2021)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction.- “Doing” Mathematics: A Toolkit for Mathematical Reasoning.- Sets and Their Algebras: The Stem Cells of Mathematics.- Numbers I: The Basics of Our Number System.- Arithmetic: Putting Numbers to Work.- Summations: Complex Operations from Simple Components.- The Vertigo of Infinity: Handling the Very Large and the Infinite.- Numbers II: Building the Integers and Building with the Integers.- Recurrences: Rendering Complex Structure Manageable.- Numbers III: Operational Representations and Their Consequences.- The Art of Counting: Combinatorics, Probability, and Statistics.- Graphs I: Representing Relationships Mathematically.- Graphs II: Graphs Within Computation and Communication.- Solutions to Exercises.- App. A, Pairing Functions.- App. B, A Deeper Look at the Fibonacci Numbers.- App. C, Two Recurrence-Defined Number Families.- App. D, Signed-Digit Numerals: Carry-Free Addition.- App. E, The Diverse Delights of de Bruijn Networks.- List of Symbols.- References.- Index.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743041237335,"sku":"9783030583750","price":67.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030583750.jpg?v=1720063853"},{"product_id":"algebra-and-geometry-with-python-9783030615437","title":"Algebra and Geometry with Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book teaches algebra and geometry. The authors dedicate chapters to the key issues of matrices, linear equations, matrix algorithms, vector spaces, lines, planes, second-order curves, and elliptic curves. \u003c\/p\u003e\u003cp\u003eThe text is supported throughout with problems, and the authors have included source code in Python in the book. The book is suitable for advanced undergraduate and graduate students in computer science.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“It is most interesting to combine a classical mathematical topic with a new evolving programming language and exactly this is obtained by this book. … This material is used as a case study for their implementation for solving problems in theoretical and practical cryptography. The ‘roadmap’ of the content of this also quite interesting.” (Panayiotis Vlamos, zbMATH 1480.00002, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eMatrices and Matrix Algorithms.- Matrix Algebra.- Systems of Linear Equations.- Complex Numbers and Matrices.- Vector Spaces.- Vectors in a Three-Dimensional Space.- Equation of a Straight Line on a Plane.- Equation of a Plane in Space.- Equation of a Line in Space.- Bilinear and Quadratic Forms.- Curves of the Second-Order.- Elliptic Curves.- Appendix A, Basic Operators in Python and C.- Appendix B, Trigonometric Formulae.- Appendix C, The Greek Alphabet.- References.- Name Index.- Subject Index.\u003cbr\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743042023767,"sku":"9783030615437","price":54.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"principles-of-parallel-scientific-computing-a-first-guide-to-numerical-concepts-and-programming-methods-9783030761936","title":"Principles of Parallel Scientific Computing: A","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eNew insight in many scientific and engineering fields is unthinkable without the use of numerical simulations running efficiently on modern computers. The faster we get new results, the bigger and accurate are the problems that we can solve. It is the combination of mathematical ideas plus efficient programming that drives the progress in many disciplines. Future champions in the area thus will have to be qualified in their application domain, they will need a profound understanding of some mathematical ideas, and they need the skills to deliver fast code.\u003c\/p\u003e  \u003cp\u003eThe present textbook targets students which have programming skills already and do not shy away from mathematics, though they might be educated in computer science or an application domain. It introduces the basic concepts and ideas behind applied mathematics and parallel programming that we need to write numerical simulations for today’s multicore workstations. Our intention is not to dive into one particular application domain or to introduce a new programming language – we lay the generic foundations for future courses and projects in the area.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eThe text is written in an accessible style which is easy to digest for students without years and years of mathematics education. It values clarity and intuition over formalism, and uses a simple N-body simulation setup to illustrate basic ideas that are of relevance in various different subdomains of scientific computing. Its primary goal is to make theoretical and paradigmatic ideas accessible to undergraduate students and to bring the fascination of the field across.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. The Pillars of Science.- 2. Moore Myths.- 3. Our Model Problem.- 4. Floating Point Numbers.- 5. A Simplistic Machine Model.- 6. Round-off Error Propagation.- 7. SIMD Vector Crunching.- 8. Arithmetic Stability of an Implementation.- 9. Vectorisation of the Model Problem.- 10. Conditioning and Well-posedness.- 11. Taylor Expansion.- 12. Ordinary Differential Equations.- 13. Accuracy and Appropriateness of Numerical Schemes.- 14. Writing Parallel Codes.- 15. Upscaling Methods.- 16. OpenMP Primer.- 17. Shared Memory Tasking.- 18. GPGPUs with OpenMP.- 19. Higher Order Methods.- 20. Adaptive Time Stepping.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743048741207,"sku":"9783030761936","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030761936.jpg?v=1720063884"},{"product_id":"algebraic-graph-algorithms-a-practical-guide-using-python-9783030878857","title":"Algebraic Graph Algorithms: A Practical Guide Using Python","description":"\u003cp\u003eThis textbook discusses the design and implementation of basic algebraic graph algorithms, and algebraic graph algorithms for complex networks, employing matroids whenever possible. The text describes the design of a simple parallel matrix algorithm kernel that can be used for parallel processing of algebraic graph algorithms. Example code is presented in pseudocode, together with case studies in Python and MPI. The text assumes readers have a background in graph theory and\/or graph algorithms.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743056114007,"sku":"9783030878856","price":32.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"logic-functions-and-equations-fundamentals-and-applications-using-the-xboole-monitor-9783030889470","title":"Logic Functions and Equations: Fundamentals and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe greatly expanded and updated 3rd edition of this textbook offers the reader a comprehensive introduction to the concepts of logic functions and equations and their applications across computer science and engineering. The authors’ approach emphasizes a thorough understanding of the fundamental principles as well as numerical and computer-based solution methods. The book provides insight into applications across propositional logic, binary arithmetic, coding, cryptography, complexity, logic design, and artificial intelligence.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eUpdated throughout, some major additions for the 3rd edition include:\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ea new chapter about the concepts contributing to the power of XBOOLE;\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003ea new chapter that introduces into the application of the XBOOLE-Monitor XBM 2;\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003emany tasks that support the readers in amplifying the learned content at the end of the chapters;\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003esolutions of a large subset of these tasks to confirm learning success;\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003echallenging tasks that need the power of the XBOOLE software for their solution.\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe XBOOLE-monitor XBM 2 software is used to solve the exercises; in this way the time-consuming and error-prone manipulation on the bit level is moved to an ordinary PC, more realistic tasks can be solved, and the challenges of thinking about algorithms leads to a higher level of education.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePart I Theoretical Foundations\u003c\/p\u003e  \u003cp\u003e1. Basic Algebraic Structures \u003c\/p\u003e  \u003cp\u003e2. Logic Functions \u003c\/p\u003e  \u003cp\u003e3. Logic Equations \u003c\/p\u003e  \u003cp\u003e4. Boolean Differential Calculus \u003c\/p\u003e  \u003cp\u003e5. Sets, Lattices, and Classes Logic Functions\u003c\/p\u003e  \u003cp\u003ePart II Applications\u003c\/p\u003e  \u003cp\u003e6. Logics, Arithmetic, and Special Functions\u003c\/p\u003e  \u003cp\u003e7. SAT-Problems\u003c\/p\u003e  \u003cp\u003e8. Extremely Complex Problems \u003c\/p\u003e  \u003cp\u003e9. Combinational Circuits \u003c\/p\u003e  \u003cp\u003e10. Sequential Circuits\u003c\/p\u003e  \u003cp\u003eReferences\u003c\/p\u003e  \u003cp\u003eIndex\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743056441687,"sku":"9783030889470","price":59.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"deep-generative-modeling-9783030931575","title":"Deep Generative Modeling","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective \"deep\" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eDeep Generative Modeling\u003c\/i\u003e is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github.\u003c\/p\u003e  \u003cp\u003eThe ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eWhy Deep Generative Modeling?.- Autoregressive Models.- Flow-based Models.- Latent Variable Models.- Hybrid Modeling.- Energy-based Models.- Generative Adversarial Networks.- Deep Generative Modeling for Neural Compression.- Useful Facts from Algebra and Calculus.- Useful Facts from Probability Theory and Statistics.- Index.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743059161431,"sku":"9783030931575","price":53.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030931575.jpg?v=1720063931"},{"product_id":"python-for-probability-statistics-and-machine-learning-9783031046476","title":"Python for Probability, Statistics, and Machine","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with \"Programming Tips\" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers.\u003c\/p\u003e\u003cp\u003e Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias\/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples.  This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Part 1 Getting Started with Scientific Python.- Installation and Setup.- Numpy.- Matplotlib.- Ipython.- Jupyter Notebook.- Scipy.- Pandas.- Sympy.- Interfacing with Compiled Libraries.- Integrated Development Environments.- Quick Guide to Performance and Parallel Programming.- Other Resources.- Part 2 Probability.- Introduction.- Projection Methods.- Conditional Expectation as Projection.- Conditional Expectation and Mean Squared Error.- Worked Examples of Conditional Expectation and Mean Square Error Optimization.- Useful Distributions.- Information Entropy.- Moment Generating Functions.- Monte Carlo Sampling Methods.- Useful Inequalities.- Part 3 Statistics.- Python Modules for Statistics.- Types of Convergence.- Estimation Using Maximum Likelihood.- Hypothesis Testing and P-Values.- Confidence Intervals.- Linear Regression.- Maximum A-Posteriori.- Robust Statistics.- Bootstrapping.- Gauss Markov.- Nonparametric Methods.- Survival Analysis.- Part 4 Machine Learning.- Introduction.- Python Machine Learning Modules.- Theory of Learning.- Decision Trees.- Boosting Trees.- Logistic Regression.- Generalized Linear Models.- Regularization.- Support Vector Machines.- Dimensionality Reduction.- Clustering.- Ensemble Methods.- Deep Learning.- Notation.- References.- Index.\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743065977175,"sku":"9783031046476","price":59.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031046476.jpg?v=1720063959"},{"product_id":"algorithms-and-discrete-applied-mathematics-9th-international-conference-caldam-2023-gandhinagar-india-february-9-11-2023-proceedings-9783031252105","title":"Algorithms and Discrete Applied Mathematics: 9th","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the proceedings of the 9th International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2023, which was held in Gandhinagar, India, during February 9-11, 2023.\u003cp\u003eThe 32 papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers were organized in topical sections named: algorithms and optimization; computational geometry; game theory; graph coloring; graph connectivity; graph domination; graph matching; graph partition and graph covering.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eStable Approximation Schemes.- A whirlwind tour of intersection graph enumeration.- Graph modification problems with forbidden minors.- Algorithms \u0026amp; Optimization Efficient reductions and algorithms for Subset Product.- Optimal length cutting plane refutations of integer programs.- Fault-Tolerant Dispersion Resource management in device-to-device communications.- Computational Geometry Algorithms for k-Dispersion for Points in Convex Position in the Plane.- Arbitrary oriented color spanning region for line segments.- Games with a Simple Rectilinear Obstacle in Plane.- Diverse Fair Allocations: Complexity and Algorithms.- Graph Coloring New bounds and constructions for neighbor-locating colorings of graphs.- D K 5-list coloring toroidal 6-regular triangulations in linear time.- On Locally Identifying Coloring of Graphs.- On Structural Parameterizations of Star Coloring.- Reddy Perfectness of G-generalized join of graphs.- Coloring of a superclass of 2K2-free graphs.- The Weak (2,2)-Labelling Problem for graphs with forbidden induced structures.- Graph Connectivity Short cycles dictate dichotomy status of the Steiner tree problem on Bisplit graphs.- Some insights on dynamic maintenance of Gomory-Hu tree in cactus graphs and general graphs.- Monitoring edge-geodetic sets in graphs.- Cyclability, Connectivity and Circumference.- Graph Domination On three domination-based identification problems in block graphs.- Graph modification problems with forbidden minors.- Computational Aspects of Double Dominating Sequences in Graph.- Relation between broadcast domination and multipacking numbers on chordal graphs.- Pushing Cops and Robber on Oriented Graphs.- Mind the Gap: Edge Facility Location Problems in Theory and Practice.- Complexity Results on Cosecure Domination in Graphs.- Kusum and Arti Pandey Graph Matching Latin Hexahedra and Related Combinatorial Structures.- Minimum Maximal Acyclic Matching in Proper Interval Graphs.- Graph Partition \u0026amp; Graph Covering Transitivity on subclasses of chordal graphs.- Maximum subgraph problem for 3-regular Knödel graphs and its wirelength.- Covering using Bounded Size Subgraphs.- Axiomatic characterization of the the toll walk function of some graph classes.- Structural Parameterization of Alliance Problems.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743077249367,"sku":"9783031252105","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031252105.jpg?v=1720064009"},{"product_id":"mathematics-for-computer-scientists-a-practice-oriented-approach-9783658404222","title":"Mathematics for Computer Scientists: A","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook contains the mathematics needed to study computer science in application-oriented computer science courses. The content is based on the author's many years of teaching experience.\u003c\/p\u003e\u003cp\u003eThe translation of the original German 7\u003csup\u003eth\u003c\/sup\u003e edition \u003ci\u003eMathematik für Informatiker\u003c\/i\u003e by Peter Hartmann was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTextbook Features\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eYou will always find applications to computer science in this book.\u003c\/li\u003e\n\u003cli\u003eNot only will you learn mathematical methods, you will gain insights into the ways of mathematical thinking to form a foundation for understanding computer science.\u003c\/li\u003e\n\u003cli\u003eProofs are given when they help you learn something, not for the sake of proving.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eMathematics is initially a necessary evil for many students. The author explains in each lesson how students can apply what they have learned by giving many real world examples, and by constantly cross-referencing math and computer science. Students will see how math is not only useful, but can be interesting and sometimes fun.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThe Content\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eSets, logic, number theory, algebraic structures, cryptography, vector spaces, matrices, linear equations and mappings, eigenvalues, graph theory.\u003c\/li\u003e\n\u003cli\u003eSequences and series, continuous functions, differential and integral calculus, differential equations, numerics.\u003c\/li\u003e\n\u003cli\u003eProbability theory and statistics.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThe Target Audiences\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eStudents in all computer science-related coursework, and independent learners.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eDISCRETE MATHEMATICS AND LINEAR ALGEBRA.- Sets and mappings.- Logic.- Natural numbers, complete induction, recursion.- Some number theory.- Algebraic structures.- Vector spaces.- Matrices.- Gaussian algorithm and systems of linear equations.- Eigenvalues, eigenvectors and basis transformations.- Scalar product and orthogonal maps.- Graph theory.- ANALYSIS.- The real numbers.- Sequences and series.- Continuous functions.- Differential calculus.- Integral calculus.- Differential equations.- Numerical methods.- PROBABILITY AND STATISTICS.- Probability spaces.- Random variables.- Important distributions and stochastic processes.- Statistical methods.- Appendix.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":48743139508567,"sku":"9783658404222","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783658404222.jpg?v=1720064284"},{"product_id":"linear-algebra-and-optimization-with-applications-to-machine-learning-volume-i-linear-algebra-for-computer-vision-robotics-and-machine-learning-9789811207716","title":"Linear Algebra And Optimization With Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743276052823,"sku":"9789811207716","price":81.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811207716.jpg?v=1720064881"},{"product_id":"linear-algebra-and-optimization-with-applications-to-machine-learning-volume-ii-fundamentals-of-optimization-theory-with-applications-to-machine-learning-9789811216565","title":"Linear Algebra And Optimization With Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eVolume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743276839255,"sku":"9789811216565","price":162.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811216565.jpg?v=1720064887"},{"product_id":"monte-carlo-methods-9789811329708","title":"Monte Carlo Methods","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“This monograph ... is intended to be a textbook for graduate students in statistics, computer science and engineering. It covers a very broad range of topics ... . Each chapter is finished by a rather long list of relevant references. Thus, it can be used also as a reference book by researches in the fields of machine learning, pattern recognition ... . it can be a useful reference to many important Monte Carol methods.” (Jaromír Antoch, zbMATH 1483.65001, 2022)\u003c\/p\u003e\u003cp\u003e“True to its goal, the text offers a comprehensive overview on Monte Carlo methods. … this text is a quality reference for researchers interested in computer vision, computer graphics, machine learning, artificial intelligence and related fields.” (Grant Innerst, MAA Reviews, July 18, 2021)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Introduction to Monte Carlo Methods.- 2 Sequential Monte Carlo.- 3 Markov Chain Monte Carlo - the Basics.- 4 Metropolis Methods and Variants.- 5 Gibbs Sampler and its Variants.- 6 Cluster Sampling Methods.- 7 Convergence Analysis of MCMC.- 8 Data Driven Markov Chain Monte Carlo.- 9 Hamiltonian and Langevin Monte Carlo.- 10 Learning with Stochastic Gradient.- 11 Mapping the Energy Landscape.","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":48743287619927,"sku":"9789811329708","price":89.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811329708.jpg?v=1720064936"},{"product_id":"pattern-classification-9780471056690","title":"Pattern Classification","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003ePattern recognition is the construction of algorithms to decode and recognize images or data patterns in so-called random data. It is a vital and growing field with applications in artifical intelligence, machine learing, data mining, speech recognition, bioinformatics, and computer vision.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"...it provides a good introduction to the subject of Pattern Classification.\" (Journal of Classification, September 2007)\u003cbr\u003e \u003cbr\u003e \"...a fantastic book! The presentation...could not be better, and I recommend that future authors consider...this book as a role model.\" (Journal of Statistical Computation and Simulation, March 2006)\u003cbr\u003e \u003cbr\u003e \"...strongly recommended both as a professional reference and as a text for students...\" (Technometrics, February 2002)\u003cbr\u003e \u003cbr\u003e \"...provides information needed to choose the most appropriate of the many available technique for a given class of problems.\" (SciTech Book News, Vol. 25, No. 2, June 2001)\u003cbr\u003e \u003cbr\u003e \"I do not believe anybody wishing to teach or do serious work on Pattern Recognition can ignore this book, as it is the sort of book one wishes to find the time to read from cover to cover!\" (Pattern Analysis \u0026amp; Applications Journal, 2001)\u003cbr\u003e \u003cbr\u003e \"This book is the unique text\/professional reference for any serious student or worker in the field of pattern recognition.\" (Mathematical Reviews, Issue 2001k)\u003cbr\u003e \u003cbr\u003e \"...gives a systematic overview about the major topics in pattern recognition, based whenever possible on fundamental principles.\" (Zentralblatt MATH, Vol. 968, 2001\/18)\u003cbr\u003e \u003cbr\u003e \"attractively presented and readable\" (Journal of Classification, Vol.18, No.2 2001)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eBayesian Decision Theory.\u003cbr\u003e \u003cbr\u003e Maximum-Likelihood and Bayesian Parameter Estimation.\u003cbr\u003e \u003cbr\u003e Nonparametric Techniques.\u003cbr\u003e \u003cbr\u003e Linear Discriminant Functions.\u003cbr\u003e \u003cbr\u003e Multilayer Neural Networks.\u003cbr\u003e \u003cbr\u003e Stochastic Methods.\u003cbr\u003e \u003cbr\u003e Nonmetric Methods.\u003cbr\u003e \u003cbr\u003e Algorithm-Independent Machine Learning.\u003cbr\u003e \u003cbr\u003e Unsupervised Learning and Clustering.\u003cbr\u003e \u003cbr\u003e Appendix.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48864644071767,"sku":"9780471056690","price":136.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471056690.jpg?v=1722272870"},{"product_id":"essential-math-for-data-science-9781098102937","title":"Essential Math for Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eTo succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866330640727,"sku":"9781098102937","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098102937.jpg?v=1722278163"},{"product_id":"scientific-computing-for-scientists-and-engineers-9783110999617","title":"Scientific Computing: For Scientists and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e \u003cem\u003eScientific Computing for Scientists and Engineers\u003c\/em\u003e is designed to teach undergraduate students relevant numerical methods and required fundamentals in scientific computing. \u003c\/p\u003e \u003cp\u003e Most problems in science and engineering require the solution of mathematical problems, most of which can only be done on a computer. Accurately approximating those problems requires solving differential equations and linear systems with millions of unknowns, and smart algorithms can be used on computers to reduce calculation times from years to minutes or even seconds. This book explains: How can we approximate these important mathematical processes? How accurate are our approximations? How efficient are our approximations? \u003c\/p\u003e \u003cp\u003e \u003cem\u003eScientific Computing for Scientists and Engineers\u003c\/em\u003e covers: \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e An introduction to a wide range of numerical methods for linear systems, eigenvalue problems, differential equations, numerical integration, and nonlinear problems; \u003c\/li\u003e\n\u003cli\u003e Scientific computing fundamentals like floating point representation of numbers and convergence; \u003c\/li\u003e\n\u003cli\u003e Analysis of accuracy and efficiency; \u003c\/li\u003e\n\u003cli\u003e Simple programming examples in MATLAB to illustrate the algorithms and to solve real life problems; \u003c\/li\u003e\n\u003cli\u003e Exercises to reinforce all topics. \u003c\/li\u003e\n\u003c\/ul\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48889051480407,"sku":"9783110999617","price":17.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110999617.jpg?v=1722552453"},{"product_id":"an-invitation-to-combinatorics-9781108476546","title":"An Invitation to Combinatorics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eActive student engagement is key to this classroom-tested combinatorics text, boasting 1200+ carefully designed problems, ten mini-projects, section warm-up problems, and chapter opening problems. The author  an award-winning teacher  writes in a conversational style, keeping the reader in mind on every page. Students will stay motivated through glimpses into current research trends and open problems as well as the history and global origins of the subject. All essential topics are covered, including Ramsey theory, enumerative combinatorics including Stirling numbers, partitions of integers, the inclusion-exclusion principle, generating functions, introductory graph theory, and partially ordered sets. Some significant results are presented as sets of guided problems, leading readers to discover them on their own. More than 140 problems have complete solutions and over 250 have hints in the back, making this book ideal for self-study. Ideal for a one semester upper undergraduate course, prerequisites include the calculus sequence and familiarity with proofs.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'I would certainly accept this 'invitation.' The text covers essentially all of the basic combinatorial subjects in a both gentle and intense way. The extensive problems, examples, and 'projects,' especially the collaborative projects, exemplify current pedagogical research on effective teaching methods. I would expect it to remain as a reference on many shelves.' Bruce Rothschild, University of California, Los Angeles\u003cbr\u003e'Shahriari's voice as an experienced classroom teacher shines through in this brilliantly crafted student-friendly text. Each mini-project provides a guided exploration of an interesting topic in combinatorics. These, together with the plethora of interesting exercises, help the student to build problem-solving muscle and to experience the joy of mathematical discovery.' Jamie Pommersheim, Reed College\u003cbr\u003e'From well-chosen motivating problems in the introduction to deeper material near the book's conclusion, Shahriari invites students encountering combinatorics systematically for the first time to think, to build, and to play. His warm writing style and cross-cultural approach to core topics of the field are sure to engage readers from many backgrounds and levels of preparation.' Joshua Cooper, University of South Carolina\u003cbr\u003e'This book is a mathematically rigorous introductory textbook on combinatorics. It contains an excellent range of problems and exercises that will help students practice and learn the material. It also lists open questions in combinatorics so students can see that the field continues to develop. The really special feature of this book is a lovely collection of mini-projects that let students explore a variety of topics and deepen their understanding.' David Auckly, Kansas State University\u003cbr\u003e'I highly recommend this text. Among its most interesting, unusual, and valuable features, one finds a long list of collaborative mini-projects for students to work on in groups, together with other problems to work on individually; nice historical asides, including references to the work of non-Western mathematicians; and a very accessible conversational style. It fits well with discovery-style or problem-oriented courses on the subject.' William Monty McGovern, University of Washington\u003cbr\u003e'One of the major attractions of this textbook is the writing style - it is designed to be very readable, as though the author were having a conversation with the reader. The result is a text which feels engaging - a quality which is sure to be of great benefit to undergraduate students.' Audie Warren, zbMATH\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Introduction; 1. Induction and Recurrence Relations; 2. The Pigeonhole Principle and Ramsey Theory; 3. Counting, Probability, Balls and Boxes; 4. Permutations and Combinations; 5. Binomial and Multinomial Coefficients; 6. Stirling Numbers; 7. Integer Partitions; 8. The Inclusion-Exclusion Principle; 9. Generating Functions; 10. Graph Theory; 11. Posets, Matchings, and Boolean Lattices; Appendices; Bibliography; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":49083829485911,"sku":"9781108476546","price":54.13,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108476546.jpg?v=1725550148"},{"product_id":"the-mathematics-of-data-9781470435752","title":"The Mathematics of Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eData science is a highly interdisciplinary field, incorporating ideas from applied mathematics, statistics, probability, and computer science, as well as many other areas. This book provides an introduction to the mathematical methods that form the foundations of machine learning and data science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eP. Drineas and M. W. Mahoney, Lectures on randomized numerical linear algebra\u003c\/li\u003e\n\u003cli\u003eS. J. Wright, Optimization algorithms for data analysis\u003c\/li\u003e\n\u003cli\u003eJ. C. Duchi, Introductory lectures on stochastic optimization\u003c\/li\u003e\n\u003cli\u003eP.-G. Martinsson, Randomized methods for matrix computations\u003c\/li\u003e\n\u003cli\u003eR. Vershynin, Four lectures on probabilistic methods for data science\u003c\/li\u003e\n\u003cli\u003eR. Ghrist, Homological algebra and data.\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\u003c\/ul\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"American Mathematical Society","offers":[{"title":"Default Title","offer_id":49083992015191,"sku":"9781470435752","price":98.1,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781470435752.jpg?v=1725550696"},{"product_id":"statistics-for-data-scientists-an-introduction-to-probability-statistics-and-data-analysis-9783030105303","title":"Statistics for Data Scientists: An Introduction","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. \u003c\/p\u003eWhere contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“Having taught data analytics at the introductory graduate level, I welcome the authors’ textbook as an essential resource for training well-grounded entry-level data scientists. … A data scientist shall provide competent data science professional services to a client. … Training in both the theory and practice of data analytics is a requirement for such competence. The authors’ textbook definitely provides a valuable resource for such training.” (Harry J. Foxwell, Computing Reviews, July 7, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 A First Look at Data.- 2 Sampling Plans and Estimates.- 3 Probability Theory.- 4 Random Variables and Distributions.- 5 Estimation.- 6 Multiple Random Variables.- 7 Making Decisions in Uncertainty.- 8 Bayesian Statistics.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49084748169559,"sku":"9783030105303","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030105303.jpg?v=1725553218"},{"product_id":"logical-methods-the-art-of-thinking-abstractly-and-mathematically-9783030637767","title":"Logical Methods: The Art of Thinking Abstractly","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMany believe mathematics is only about calculations, formulas, numbers, and strange letters. But mathematics is much more than just crunching numbers or manipulating symbols. Mathematics is about discovering patterns, uncovering hidden structures, finding counterexamples, and thinking logically. Mathematics is a way of thinking. It is an activity that is both highly creative and challenging. \u003cp\u003eThis book offers an introduction to mathematical reasoning for beginning university or college students, providing a solid foundation for further study in mathematics, computer science, and related disciplines. Written in a manner that directly conveys the sense of excitement and discovery at the heart of doing science,  its 25 short and visually appealing chapters cover the basics of set theory, logic, proof methods, combinatorics, graph theory, and much more.\u003c\/p\u003e  \u003cp\u003eIn the book you will, among other things, find answers to:\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eWhat is a proof? What is a counterexample?\u003c\/li\u003e\n\u003cli\u003eWhat does it mean to say that something follows logically from a set of premises?\u003c\/li\u003e\n\u003cli\u003eWhat does it mean to abstract over something?\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eHow can knowledge and information be represented and used in calculations?\u003c\/li\u003e\n\u003cli\u003eWhat is the connection between Morse code and Fibonacci numbers?\u003c\/li\u003e\n\u003cli\u003eWhy could it take billions of years to solve Hanoi's Tower?\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eLogical Methods\u003c\/i\u003e is especially appropriate for students encountering such concepts for the very first time. Designed to ease the transition to a university or college level study of mathematics or computer science, it also provides an accessible and fascinating gateway to logical thinking for students of all disciplines.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"The definitions are followed by examples to help explain their meaning, along with counterexamples ... . Therefore, very little basic knowledge is required for this introduction to logical methods ... which is written in an accessible style ... . contained in the book are several hundred small figures; arrow, Venn, and Hasse diagrams; and simplifies visual representations ... . The author has also elected to use color to draw the reader's attention ... .\" \u003cbr\u003e“From personal teaching experience, knowledge of these mathematical areas is necessary for disparate fields of CS and informatics. These foundations are needed for many fields, from database theory to various domains of information systems applications. The book’s presentation of topics and incentives for problem-solving, along with its exercises, is very useful for university-level instructors and students. The compact chapters contain clear explanations, diagrams, and brief descriptions of interesting facts.” (Bálint Molnár, Computing Reviews, July 27, 2021)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.- 0 The Art of Thinking Abstractly and Mathematically.- 1 Basic Set Theory.- 2 Propositional Logic.- 3 Semantics from Propositional Logic.- 4 Concepts in Propositional Logic.- 5 Proofs, Conjectures, and Counterexamples.- 6 Relations.- 7 Functions.- 8 A Little More Set Theory.- 9 Closures and Inductively Defined Sets.- 10 Recursively Defined Functions.- 11 Mathematical Induction.- 12 Structural Induction.- 13 First-Order Languages.- 14 Representation of Quantified Statements.- 15 Interpretation in Models.- 16 Reasoning About Models.- 17 Abstraction with Equivalences and Partitions.- 18 Combinatorics.- 19 A Little More Combinatorics.- 20 A Bit of Abstract Algebra.- 21 Graph Theory.- 22 Walks in Graphs.- 23 Formal Languages and Grammars.- 24 Natural Deduction.- The Road Ahead.- Index. Symbols.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49084751479127,"sku":"9783030637767","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030637767.jpg?v=1725553226"},{"product_id":"introduction-to-computation-haskell-logic-and-automata-9783030769079","title":"Introduction to Computation: Haskell, Logic and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eComputation, itself a form of calculation, incorporates steps that include arithmetical and non-arithmetical (logical) steps following a specific set of rules (an algorithm).  This uniquely accessible textbook introduces students using a very distinctive approach, quite rapidly leading them into essential topics with sufficient depth, yet in a highly intuitive manner.  From core elements like sets, types, Venn diagrams and logic, to patterns of reasoning, calculus, recursion and expression trees, the book spans the breadth of key concepts and methods that will enable students to readily progress with their studies in Computer Science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This book is intended as a textbook for an introductory course in computation for students beginning in informatics. No prerequisites are needed, all concepts, even elementary ones ... . it is also very suited for self-study, even if a reader is interested in Haskell or symbolic logic alone. ... Comprehension is supported by exercises for each chapter ... .” (Dieter Riebesehl, zbMATH 1497.68005, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Sets 132 Types 193 Simple Computations 274 Venn Diagrams and Logical Connectives 355 Lists and Comprehensions 456 Features and Predicates 557 Testing Your Programs 638 Patterns of Reasoning 739 More Patterns of Reasoning 8110 Lists and Recursion 9111 More Fun with Recursion 10112 Higher-Order Functions 11113 Higher and Higher 12314 Sequent Calculus 13115 Algebraic Data Types 14316 Expression Trees 15717 Karnaugh Maps 17518 Relations and Quantifiers 18319 Checking Satisfiability 19120 Data Representation 20321 Data Abstraction 22122 Efficient CNF Conversion 23723 Counting Satisfying Valuations 24924 Type Classes 26325 Search in Trees 27526 Combinatorial Algorithms 28527 Finite Automata 29928 Deterministic Finite Automata 31129 Non-Deterministic Finite Automata 32130 Input\/Output and Monads 34131 Regular Expressions 35932 Non-Regular Languages 369Index 377","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49084751577431,"sku":"9783030769079","price":28.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030769079.jpg?v=1725553223"},{"product_id":"discrete-mathematics-a-concise-introduction-9783031304873","title":"Discrete Mathematics: A Concise Introduction","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book is ideal for a first or second year discrete mathematics course for mathematics, engineering, and computer science majors. The author has extensively class-tested early conceptions of the book over the years and supplements mathematical arguments with informal discussions to aid readers in understanding the presented topics. “Safe” – that is, paradox-free – informal set theory is introduced following on the heels of Russell’s Paradox as well as the topics of finite, countable, and uncountable sets with an exposition and use of Cantor’s diagonalisation technique. Predicate logic “for the user” is introduced along with axioms and rules and extensive examples. Partial orders and the \u003ci\u003eminimal condition\u003c\/i\u003e are studied in detail with the latter shown to be equivalent to the \u003ci\u003einduction principle\u003c\/i\u003e. Mathematical induction is illustrated with several examples and is followed by a thorough exposition of inductive definitions of \u003ci\u003efunctions\u003c\/i\u003e \u003ci\u003eand\u003c\/i\u003e \u003ci\u003esets\u003c\/i\u003e. Techniques for solving recurrence relations including generating functions, the O- and o-notations, and trees are provided. Over 200 end of chapter exercises are included to further aid in the understanding and applications of discrete mathematics. \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eElementary Informal Set Theory.- Safe Set Theory.- Relations and Functions.- A Tiny Bit of Informal Logic.- Inductively Defined Sets and Structural Induction.- Recurrence Equations.- Trees and Graphs.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49084755738967,"sku":"9783031304873","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031304873.jpg?v=1725553236"},{"product_id":"category-theory-invariances-and-symmetries-in-computer-science-9783111080567","title":"Category Theory: Invariances and Symmetries in","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book analyzes the generation of the arrow-categories of a given category, which is a foundational and distinguishable Category Theory phenomena, in analogy to the foundational role of sets in the traditional set-based Mathematics, for defi nition of natural numbers as well. This inductive transformation of a category into the infinite hierarchy of the arrowcategories is extended to the functors and natural transformations. The author considers invariant categorial properties (the symmetries) under such inductive transformations. The book focuses in particular on Global symmetry (invariance of adjunctions) and Internal symmetries between arrows and objects in a category (in analogy to Field Theories like Quantum Mechanics and General Relativity). The second part of the book is dedicated to more advanced applications of Internal symmetry to Computer Science: for Intuitionistic Logic, Untyped Lambda Calculus with Fixpoint Operators, Labeled Transition Systems in Process Algebras and Modal logics as well as Data Integration Theory. \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":49084764356951,"sku":"9783111080567","price":129.67,"currency_code":"GBP","in_stock":true}]},{"product_id":"probability-and-statistics-for-computer-science-9780470383421","title":"Probability and Statistics for Computer Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis title develops introductory topics in probability and statistics with particular emphasis on concepts that arise in computer science. It starts with the basic definitions of probability distributions and random variables and elaborates their properties and applications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Undoubtedly, this is an excellent and well-organized book.\" (\u003ci\u003eComputing Reviews\u003c\/i\u003e, August 27, 2008)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003e\u003cb\u003e1. Combinatorics and Probability.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Combinatorics.\u003c\/p\u003e \u003cp\u003e1.2 Summations.\u003c\/p\u003e \u003cp\u003e1.3 Probability spaces and random variables.\u003c\/p\u003e \u003cp\u003e1.4 Conditional probability.\u003c\/p\u003e \u003cp\u003e1.5 Joint distributions.\u003c\/p\u003e \u003cp\u003e1.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Discrete Distributions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The Bernoulli and binomial distributions.\u003c\/p\u003e \u003cp\u003e2.2 Power series.\u003c\/p\u003e \u003cp\u003e2.3 Geometric and negative binomial forms.\u003c\/p\u003e \u003cp\u003e2.4 The Poisson distribution.\u003c\/p\u003e \u003cp\u003e2.5 The hypergeometric distribution.\u003c\/p\u003e \u003cp\u003e2.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Simulation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Random number generation.\u003c\/p\u003e \u003cp\u003e3.2 Inverse transforms and rejection filters.\u003c\/p\u003e \u003cp\u003e3.3 Client-server systems.\u003c\/p\u003e \u003cp\u003e3.4 Markov chains.\u003c\/p\u003e \u003cp\u003e3.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Discrete Decision Theory.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Decision methods without samples.\u003c\/p\u003e \u003cp\u003e4.2 Statistics and their properties.\u003c\/p\u003e \u003cp\u003e4.3 Sufficient statistics.\u003c\/p\u003e \u003cp\u003e4.4 Hypothesis testing.\u003c\/p\u003e \u003cp\u003e4.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Real Line-Probability.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 One-dimensional real distributions.\u003c\/p\u003e \u003cp\u003e5.2 Joint random variables.\u003c\/p\u003e \u003cp\u003e5.3 Differentiable distributions.\u003c\/p\u003e \u003cp\u003e5.4 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Continuous Distributions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 The normal distributions.\u003c\/p\u003e \u003cp\u003e6.2 Limit theorems.\u003c\/p\u003e \u003cp\u003e6.3 Gamma and beta distributions.\u003c\/p\u003e \u003cp\u003e6.4 The X\u003csup\u003e2\u003c\/sup\u003e and related distributions.\u003c\/p\u003e \u003cp\u003e6.5 Computer simulations.\u003c\/p\u003e \u003cp\u003e6.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Parameter Estimation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Bias, consistency, and efficiency.\u003c\/p\u003e \u003cp\u003e7.2 Normal inference.\u003c\/p\u003e \u003cp\u003e7.3 Sums of squares.\u003c\/p\u003e \u003cp\u003e7.4 Analysis of variance.\u003c\/p\u003e \u003cp\u003e7.5 Linear regression.\u003c\/p\u003e \u003cp\u003e7.6 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA. Analytical Tools.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB. Statistical Tables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402319634775,"sku":"9780470383421","price":109.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470383421.jpg?v=1730480059"},{"product_id":"probability-and-statistics-for-computer-science-9780471326724","title":"Probability and Statistics for Computer Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis title develops introductory topics in probability and statistics with particular emphasis on concepts that arise in computer science. It starts with the basic definitions of probability distributions and random variables and elaborates their properties and applications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"This text will fill a gap in the education of a sophisticated computer science student who has a firm base in mathematics and statistics.\" (\u003ci\u003eComputing Reviews,\u003c\/i\u003e May 7, 2009)  \u003cp\u003e\"…this textbook would be ideal.\" (\u003ci\u003eThe American Statistician\u003c\/i\u003e, February 2006)\u003c\/p\u003e \u003cp\u003e\"This is really a statistics textbook written explicitly for undergraduate computer science majors…I found the numerous examples of the use of statistics within the field of computer science extremely informative.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, November 2004)\u003c\/p\u003e \u003cp\u003e\"Thorough, in-depth, relatively complete and rigorous introduction to the statistics a CS professional should know.\" (\u003ci\u003eAmerican Mathematical Monthly\u003c\/i\u003e, August 2004)\u003c\/p\u003e \u003cp\u003e\"This is a rigorous introductory text in probability and statistics, which also develops in a rigorous fashion all the necessary supporting mathematics beyond calculus and algebra.\" (\u003ci\u003eMathematical Reviews\u003c\/i\u003e, issue 2004i)\u003c\/p\u003e \u003cp\u003e\"...one-of-a-kind resource...proves an ideal resource for computer science students and practitioners interested in a probability study...\" (\u003ci\u003eZentralblatt Math\u003c\/i\u003e, Vol. 1027, 2004)\u003c\/p\u003e \u003cp\u003e“...presents introductory topics in probability and statistics with particular emphasis on concepts that arise in computer science...disguised also by the feature that it develops all necessary supporting mathematics in a thorough and rigorous fashion.” (\u003ci\u003eQuarterly of Applied Mathematics\u003c\/i\u003e, Vol. LXI, No. 4, December 2003)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003e1. Combinatorics and Probability.\u003c\/p\u003e \u003cp\u003e2. Discrete Distributions.\u003c\/p\u003e \u003cp\u003e3. Simulation.\u003c\/p\u003e \u003cp\u003e4. Discrete Decision Theory.\u003c\/p\u003e \u003cp\u003e5. Real Line-Probability.\u003c\/p\u003e \u003cp\u003e6. Continuous Distributions.\u003c\/p\u003e \u003cp\u003e7. Parameter Estimation.\u003c\/p\u003e \u003cp\u003eAppendix A. Analytical Tools.\u003c\/p\u003e \u003cp\u003eAppendix B. Statistical Tables.\u003c\/p\u003e \u003cp\u003eBibliography.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402564608343,"sku":"9780471326724","price":209.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471326724.jpg?v=1730480774"},{"product_id":"computational-methods-in-physics-chemistry-and-biology-an-introduction-9780471495628","title":"Computational Methods in Physics Chemistry and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProviding an accessible introduction to a range of modern computational techniques, this volume is perfect for anyone with only a limited knowledge of physics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"? Dieses Buch mit seinem klar eingegrenzten Themenspektrum ist ausgezeichnet - gut lesbar und informativ zugleich!\" Chemistry in Britain \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.\u003cbr\u003e \u003cbr\u003e Acknowledgments.\u003cbr\u003e \u003cbr\u003e About the Author.\u003cbr\u003e \u003cbr\u003e About the Book.\u003cbr\u003e \u003cbr\u003e Introduction.\u003cbr\u003e \u003cbr\u003e Numerical Solutions to Schrödinger's Equation.\u003cbr\u003e \u003cbr\u003e Approximate Methods.\u003cbr\u003e \u003cbr\u003e Matrix Methods.\u003cbr\u003e \u003cbr\u003e Deterministic Simulations.\u003cbr\u003e \u003cbr\u003e Stochastic Simulations.\u003cbr\u003e \u003cbr\u003e Percolation Theory.\u003cbr\u003e \u003cbr\u003e Evolutionary Methods.\u003cbr\u003e \u003cbr\u003e Molecular Dynamics.\u003cbr\u003e \u003cbr\u003e Appendix A: FORTRAN Implementation of the Shooting Method.\u003cbr\u003e \u003cbr\u003e Appendix B: ² in Spherical Polar Coordinates.\u003cbr\u003e \u003cbr\u003e Appendix C: A Comment on the Computer Sourcecodes.\u003cbr\u003e \u003cbr\u003e Appendix D: Note for Tutors.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402615398743,"sku":"9780471495628","price":178.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471495628.jpg?v=1730480973"},{"product_id":"computational-methods-in-physics-chemistry-and-biology-an-introduction-9780471495635","title":"Computational Methods in Physics Chemistry and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProviding an accessible introduction to a range of modern computational techniques, this book is perfect for anyone with only a limited knowledge of physics. It leads readers through a series of examples, problems, and practical--based tasks covering the basics to more complex ideas and techniques.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"within its tightly defined scope, the book is excellent, being both readable and informative\" (Chemistry in Britain, January 2002)\u003cbr\u003e \u003cbr\u003e \"...The book is fresh in its spirit...\" (Zentralblatt Math, Vol.987, No. 12, 2002)\u003cbr\u003e \u003cbr\u003e \"...an excellent book for undergraduate courses...\" (Physical Sciences Educational Reviews, November 2002)\u003cbr\u003e\"? Dieses Buch mit seinem klar eingegrenzten Themenspektrum ist ausgezeichnet - gut lesbar und informativ zugleich!\" Chemistry in Britain\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface\u003cbr\u003e \u003cbr\u003e Introduction\u003cbr\u003e \u003cbr\u003e Numerical Solutions to Schrö dinger's Equation\u003cbr\u003e \u003cbr\u003e Approximate Methods\u003cbr\u003e \u003cbr\u003e Matrix Methods\u003cbr\u003e \u003cbr\u003e Deterministic Simulations\u003cbr\u003e \u003cbr\u003e Stochastic Simulations\u003cbr\u003e \u003cbr\u003e Percolation Theory\u003cbr\u003e \u003cbr\u003e Evolutionary Methods\u003cbr\u003e \u003cbr\u003e Molecular Dynamics\u003cbr\u003e \u003cbr\u003e Appendices\u003cbr\u003e \u003cbr\u003e References\u003cbr\u003e \u003cbr\u003e Index","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402615497047,"sku":"9780471495635","price":65.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471495635.jpg?v=1730480973"},{"product_id":"computational-molecular-biology-an-introduction-wiley-series-in-mathematical-computational-biology-1-9780471872511","title":"Computational Molecular Biology An Introduction","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis introductory level text is suitable for use by advanced undergraduate and graduate students of computational biology. Written by experienced authors, it provides detailed coverage of many algorithms, including applications and possible modifications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"...much-needed introductory level text...\"  - La Doc Sti, July 2000\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eMolecular Biology.\u003cbr\u003e \u003cbr\u003e Math Primer.\u003cbr\u003e \u003cbr\u003e Sequence Alignment.\u003cbr\u003e \u003cbr\u003e All About Eve.\u003cbr\u003e \u003cbr\u003e Hidden Markov Models.\u003cbr\u003e \u003cbr\u003e Structure Prediction.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402680934743,"sku":"9780471872511","price":231.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471872511.jpg?v=1730481214"},{"product_id":"computational-molecular-biology-9780471872528","title":"Computational Molecular Biology","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eRecently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology.\u003cbr\u003e \u003cbr\u003e * Provides the background mathematics required to understand why certain algorithms work\u003cbr\u003e * Guides the reader through probability theory, entropy and combinatorial optimization\u003cbr\u003e * In-depth coverage of molecular biology and protein structure prediction\u003cbr\u003e * Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction\u003cbr\u003e * Includes class tested exercises useful for self-study\u003cbr\u003e * Source code of programs available on a Web site\u003cbr\u003e \u003cbr\u003e Primarily aimed at advanced undergrad\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"...much needed introductory level text on the subject...\" (La Doc STI, July 2000)  \u003cp\u003e\"...very concise and compact...\" (Mathematical Reviews, 2002h)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eMolecular Biology.\u003cbr\u003e \u003cbr\u003e Math Primer.\u003cbr\u003e \u003cbr\u003e Sequence Alignment.\u003cbr\u003e \u003cbr\u003e All About Eve.\u003cbr\u003e \u003cbr\u003e Hidden Markov Models.\u003cbr\u003e \u003cbr\u003e Structure Prediction.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402680967511,"sku":"9780471872528","price":77.36,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471872528.jpg?v=1730481214"},{"product_id":"connecting-discrete-mathematics-and-computer-science-9781009150491","title":"Connecting Discrete Mathematics and Computer","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis textbook is designed for undergraduate students taking a course on the mathematical foundations of computer science. It is written from an exclusively CS perspective rather than for a mixed-discipline audience, helping CS students see the ways that foundational mathematical material is central to the discipline of computer science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Finally! I've spent years struggling to find a textbook that makes the topic of Discrete Structures relevant to Computer Science students, David Liben-Nowell has put forth a book that will make CS students invested in the material. He not only connects every topic to Computer Science but does so in a clear and entertaining way.' Dan Arena, Vanderbilt University\u003cbr\u003e'Unlike most discrete math texts, here the computer science content and connections are woven extensively throughout, with “forward pointers” that can excite students about numerous computer science areas they will encounter in their future studies. In addition, the book is written TO students, not FOR faculty. It will be a joy to teach with!' Valerie Barr, Mount Holyoke College\u003cbr\u003e'By foregrounding the connections between the fields, this outstanding textbook makes a compelling case for why computer science students should embrace the study of discrete mathematics. This is an approachable yet rigorous book, written with wit and verve, that I look forward to teaching from!' Raghuram Ramanujan, Davidson College\u003cbr\u003e'David Liben-Nowell's Connecting Discrete Mathematics and Computer Science provides students with a beautifully motivated, clearly written, and accessible exploration of the mathematical foundations of computer science. The “Computer Science Connections” sections provide compelling applications of the mathematical content and the frequent “Taking in further” notes provide extra richness that add to the joy of the experience. This is a discrete math book that truly keeps the reader engaged!' Ran Libeskind-Hadas, Founding Chair of Integrated Sciences, Claremont McKenna College\u003cbr\u003e'An inspired approach to the introductory discrete math course, illuminating the aesthetic appeal of the subject together with the profound and inextricable links that connect it to the core ideas of computing.' Jon Kleinberg, Cornell University\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. On the point of this book; 2. Basic data types; 3. Logic; 4. Proofs; 5. Mathematical induction; 6. Analysis of algorithms; 7. Number theory; 8. Relations; 9. Counting; 10. Probability; 11. Graphs and trees; 12. Looking forward.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":49406645502295,"sku":"9781009150491","price":55.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781009150491.jpg?v=1730496619"},{"product_id":"proven-impossible-9781009349499","title":"Proven Impossible","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWritten for any motivated reader with a high-school knowledge of mathematics, and the discipline to follow logical arguments, this book presents the proofs for revolutionary impossibility theorems in an accessible way, with less jargon and notation, and more background, intuition, examples, explanations, and exercises.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This unique and lovely book takes us on a grand tour of the limitations of science, mathematics, and of reason itself. To appreciate what is possible we must know the impossible, and such limitations define the boundary between the two. Gusfield offers well-explained gems illustrating various limitations, showing why they arise, giving their historical context, and in contrast to other similar books for a broad audience, presenting rigorous proofs requiring limited background.' Michael Sipser, MIT\u003cbr\u003e'There are impossible problems in many different fields (e.g., Physics, Mathematics). This book is an excellent exposition of these difference ways a problem can be impossible. Along the way, the reader will pick up the needed background which is interesting in itself.' William Gasarch, University of Maryland\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; 1. Yes you can prove a negative!; 2. Bell's impossibility theorem(s); 3. Enjoying Bell magic; 4. Arrow's (and friends') impossibility theorems; 5. Clustering and impossibility; 6. Gödel-ish impossibility; 7. Turing undecidability and incompleteness; 8. Chaitin's theorem: More devastating; 9. Gödel (for real, this time).","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":49406648516951,"sku":"9781009349499","price":26.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781009349499.jpg?v=1730496631"},{"product_id":"acta-numerica-2023-volume-32-9781009419741","title":"ACTA Numerica 2023 Volume 32","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eActa Numerica is an annual publication containing invited survey papers by leading researchers in numerical mathematics and scientific computing. The papers present overviews of recent developments in their area and provide state-of-the-art techniques and analysis.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Low-rank tensor methods for partial differential equations Markus Bachmayr; 2. The virtual element method Lourenço Beirão da Veiga, Franco Brezzi, L. Donatella Marini and Alessandro Russo; 3. Floating-point arithmetic Sylvie Boldo, Claude-Pierre Jeannerod, Guillaume Melquiond and Jean-Michel Muller; 4. Compatible finite element methods for geophysical fluid dynamics Colin J. Cotter; 5. Control of port-Hamiltonian differential-algebraic systems and applications Volker Mehrmann and Benjamin Unger; 6. Overcoming the timescale barrier in molecular dynamics: transfer operators, variational principles and machine learning Christof Schütte, Stefan Klus and Carsten Hartmann; 7. Linear optimization over homogeneous matrix cones Levent Tunçel and Lieven Vandenberghe.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":49406649368919,"sku":"9781009419741","price":164.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781009419741.jpg?v=1730496634"},{"product_id":"mathematical-structures-for-computer-graphics-9781118712191","title":"Mathematical Structures for Computer Graphics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA comprehensive exploration of the mathematics behind the modeling and rendering of computer graphics scenes     Mathematical Structures for Computer Graphics presents an accessible and intuitive approach to the mathematical ideas and techniques necessary for two- and three-dimensional computer graphics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“The book is suitable for undergraduate students of computer science, mathematics, and engineering, as well as an ideal reference for researchers and professionals in computer graphics.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 1 June 2015)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Basics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Graphics Pipeline 2\u003c\/p\u003e \u003cp\u003e1.2 Mathematical Descriptions 4\u003c\/p\u003e \u003cp\u003e1.3 Position 5\u003c\/p\u003e \u003cp\u003e1.4 Distance 8\u003c\/p\u003e \u003cp\u003e1.5 Complements and Details 11\u003c\/p\u003e \u003cp\u003e1.5.1 Pythagorean Theorem Continued 11\u003c\/p\u003e \u003cp\u003e1.5.2 Law of Cosines Continued 12\u003c\/p\u003e \u003cp\u003e1.5.3 Law of Sines 13\u003c\/p\u003e \u003cp\u003e1.5.4 Numerical Calculations 13\u003c\/p\u003e \u003cp\u003e1.6 Exercises 14\u003c\/p\u003e \u003cp\u003e1.6.1 Programming Exercises 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Vector Algebra 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Basic Vector Characteristics 18\u003c\/p\u003e \u003cp\u003e2.1.1 Points Versus Vectors 20\u003c\/p\u003e \u003cp\u003e2.1.2 Addition 20\u003c\/p\u003e \u003cp\u003e2.1.3 Scalar Multiplication 21\u003c\/p\u003e \u003cp\u003e2.1.4 Subtraction 22\u003c\/p\u003e \u003cp\u003e2.1.5 Vector Calculations 22\u003c\/p\u003e \u003cp\u003e2.1.6 Properties 24\u003c\/p\u003e \u003cp\u003e2.1.7 Higher Dimensions 25\u003c\/p\u003e \u003cp\u003e2.2 Two Important Products 25\u003c\/p\u003e \u003cp\u003e2.2.1 Dot Product 25\u003c\/p\u003e \u003cp\u003e2.2.2 Cross Product 29\u003c\/p\u003e \u003cp\u003e2.3 Complements and Details 34\u003c\/p\u003e \u003cp\u003e2.3.1 Vector History 34\u003c\/p\u003e \u003cp\u003e2.3.2 More about Points Versus Vectors 35\u003c\/p\u003e \u003cp\u003e2.3.3 Vector Spaces and Affine Spaces 36\u003c\/p\u003e \u003cp\u003e2.4 Exercises 38\u003c\/p\u003e \u003cp\u003e2.4.1 Programming Exercises 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Vector Geometry 40\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Lines and Planes 40\u003c\/p\u003e \u003cp\u003e3.1.1 Vector Description of Lines 40\u003c\/p\u003e \u003cp\u003e3.1.2 Vector Description of Planes 44\u003c\/p\u003e \u003cp\u003e3.2 Distances 46\u003c\/p\u003e \u003cp\u003e3.2.1 Point to a Line 46\u003c\/p\u003e \u003cp\u003e3.2.2 Point to a Plane 48\u003c\/p\u003e \u003cp\u003e3.2.3 Parallel Planes and Line to a Plane 48\u003c\/p\u003e \u003cp\u003e3.2.4 Line to a Line 50\u003c\/p\u003e \u003cp\u003e3.3 Angles 52\u003c\/p\u003e \u003cp\u003e3.4 Intersections 54\u003c\/p\u003e \u003cp\u003e3.4.1 Intersecting Lines 54\u003c\/p\u003e \u003cp\u003e3.4.2 Lines Intersecting Planes 56\u003c\/p\u003e \u003cp\u003e3.4.3 Intersecting Planes 57\u003c\/p\u003e \u003cp\u003e3.5 Additional Key Applications 61\u003c\/p\u003e \u003cp\u003e3.5.1 Intersection of Line Segments 61\u003c\/p\u003e \u003cp\u003e3.5.2 Intersection of Line and Sphere 65\u003c\/p\u003e \u003cp\u003e3.5.3 Areas and Volumes 66\u003c\/p\u003e \u003cp\u003e3.5.4 Triangle Geometry 68\u003c\/p\u003e \u003cp\u003e3.5.5 Tetrahedron 69\u003c\/p\u003e \u003cp\u003e3.6 Homogeneous Coordinates 71\u003c\/p\u003e \u003cp\u003e3.6.1 Two Dimensions 72\u003c\/p\u003e \u003cp\u003e3.6.2 Three Dimensions 73\u003c\/p\u003e \u003cp\u003e3.7 Complements and Details 75\u003c\/p\u003e \u003cp\u003e3.7.1 Intersection of Three Planes Continued 75\u003c\/p\u003e \u003cp\u003e3.7.2 Homogeneous Coordinates Continued 77\u003c\/p\u003e \u003cp\u003e3.8 Exercises 79\u003c\/p\u003e \u003cp\u003e3.8.1 Programming Exercises 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Transformations 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Types of Transformations 84\u003c\/p\u003e \u003cp\u003e4.2 Linear Transformations 85\u003c\/p\u003e \u003cp\u003e4.2.1 Rotation in Two Dimensions 88\u003c\/p\u003e \u003cp\u003e4.2.2 Reflection in Two dimensions 90\u003c\/p\u003e \u003cp\u003e4.2.3 Scaling in Two Dimensions 92\u003c\/p\u003e \u003cp\u003e4.2.4 Matrix Properties 93\u003c\/p\u003e \u003cp\u003e4.3 Three Dimensions 95\u003c\/p\u003e \u003cp\u003e4.3.1 Rotations in Three Dimensions 95\u003c\/p\u003e \u003cp\u003e4.3.2 Reflections in Three Dimensions 101\u003c\/p\u003e \u003cp\u003e4.3.3 Scaling and Shear in Three Dimensions 102\u003c\/p\u003e \u003cp\u003e4.4 Affine Transformations 103\u003c\/p\u003e \u003cp\u003e4.4.1 Transforming Homogeneous Coordinates 105\u003c\/p\u003e \u003cp\u003e4.4.2 Perspective Transformations 107\u003c\/p\u003e \u003cp\u003e4.4.3 Transforming Normals 110\u003c\/p\u003e \u003cp\u003e4.4.4 Summary 111\u003c\/p\u003e \u003cp\u003e4.5 Complements and Details 112\u003c\/p\u003e \u003cp\u003e4.5.1 Vector Approach to Reflection in an Arbitrary Plane 113\u003c\/p\u003e \u003cp\u003e4.5.2 Vector Approach to Arbitrary Rotations 115\u003c\/p\u003e \u003cp\u003e4.6 Exercises 121\u003c\/p\u003e \u003cp\u003e4.6.1 Programming Exercises 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Orientation 124\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Cartesian Coordinate Systems 125\u003c\/p\u003e \u003cp\u003e5.2 Cameras 132\u003c\/p\u003e \u003cp\u003e5.2.1 Moving the Camera or Objects 134\u003c\/p\u003e \u003cp\u003e5.2.2 Euler Angles 137\u003c\/p\u003e \u003cp\u003e5.2.3 Quaternions 141\u003c\/p\u003e \u003cp\u003e5.2.4 Quaternion Algebra 143\u003c\/p\u003e \u003cp\u003e5.2.5 Rotations 145\u003c\/p\u003e \u003cp\u003e5.2.6 Interpolation: Slerp 148\u003c\/p\u003e \u003cp\u003e5.2.7 From Euler Angles and Quaternions to Rotation Matrices 151\u003c\/p\u003e \u003cp\u003e5.3 Other Coordinate Systems 152\u003c\/p\u003e \u003cp\u003e5.3.1 Non-orthogonal Axes 152\u003c\/p\u003e \u003cp\u003e5.3.2 Polar, Cylindrical, and Spherical Coordinates 154\u003c\/p\u003e \u003cp\u003e5.3.3 Barycentric Coordinates 157\u003c\/p\u003e \u003cp\u003e5.4 Complements and Details 158\u003c\/p\u003e \u003cp\u003e5.4.1 Historical Note: Descartes 158\u003c\/p\u003e \u003cp\u003e5.4.2 Historical Note: Hamilton 158\u003c\/p\u003e \u003cp\u003e5.4.3 Proof of Quaternion Rotation 159\u003c\/p\u003e \u003cp\u003e5.5 Exercises 161\u003c\/p\u003e \u003cp\u003e5.5.1 Programming Exercises 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Polygons and Polyhedra 164\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Triangles 164\u003c\/p\u003e \u003cp\u003e6.1.1 Barycentric Coordinates 165\u003c\/p\u003e \u003cp\u003e6.1.2 Areas and Barycentric Coordinates 166\u003c\/p\u003e \u003cp\u003e6.1.3 Interpolation 171\u003c\/p\u003e \u003cp\u003e6.1.4 Key Points in a Triangle 172\u003c\/p\u003e \u003cp\u003e6.2 Polygons 178\u003c\/p\u003e \u003cp\u003e6.2.1 Convexity 179\u003c\/p\u003e \u003cp\u003e6.2.2 Angles and Area 180\u003c\/p\u003e \u003cp\u003e6.2.3 Inside and Outside 184\u003c\/p\u003e \u003cp\u003e6.2.4 Triangulation 187\u003c\/p\u003e \u003cp\u003e6.2.5 Delaunay Triangulation 189\u003c\/p\u003e \u003cp\u003e6.3 Polyhedra 192\u003c\/p\u003e \u003cp\u003e6.3.1 Regular Polyhedra 194\u003c\/p\u003e \u003cp\u003e6.3.2 Volume of Polyhedra 196\u003c\/p\u003e \u003cp\u003e6.3.3 Euler’s Formula 200\u003c\/p\u003e \u003cp\u003e6.3.4 Rotational Symmetries 202\u003c\/p\u003e \u003cp\u003e6.4 Complements and Details 205\u003c\/p\u003e \u003cp\u003e6.4.1 Generalized Barycentric Coordinates 205\u003c\/p\u003e \u003cp\u003e6.4.2 Data Structures 206\u003c\/p\u003e \u003cp\u003e6.5 Exercises 208\u003c\/p\u003e \u003cp\u003e6.5.1 Programming Exercises 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Curves and Surfaces 212\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Curve Descriptions 213\u003c\/p\u003e \u003cp\u003e7.1.1 Lagrange Interpolation 218\u003c\/p\u003e \u003cp\u003e7.1.2 Matrix Form for Curves 222\u003c\/p\u003e \u003cp\u003e7.2 Bézier Curves 223\u003c\/p\u003e \u003cp\u003e7.2.1 Properties for Two-Dimensional Bézier Curves 226\u003c\/p\u003e \u003cp\u003e7.2.2 Joining Bézier Curve Segments 228\u003c\/p\u003e \u003cp\u003e7.2.3 Three-Dimensional Bézier Curves 229\u003c\/p\u003e \u003cp\u003e7.2.4 Rational Bézier Curves 230\u003c\/p\u003e \u003cp\u003e7.3 B-Splines 232\u003c\/p\u003e \u003cp\u003e7.3.1 Linear Uniform B-Splines 233\u003c\/p\u003e \u003cp\u003e7.3.2 Quadratic Uniform B-Splines 235\u003c\/p\u003e \u003cp\u003e7.3.3 Cubic Uniform B-Splines 240\u003c\/p\u003e \u003cp\u003e7.3.4 B-Spline Properties 242\u003c\/p\u003e \u003cp\u003e7.4 Nurbs 246\u003c\/p\u003e \u003cp\u003e7.5 Surfaces 250\u003c\/p\u003e \u003cp\u003e7.6 Complements and Details 260\u003c\/p\u003e \u003cp\u003e7.6.1 Adding Control Points to Bézier Curves 260\u003c\/p\u003e \u003cp\u003e7.6.2 Quadratic B-Spline Blending Functions 262\u003c\/p\u003e \u003cp\u003e7.7 Exercises 264\u003c\/p\u003e \u003cp\u003e7.7.1 Programming Exercises 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Visibility 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Viewing 267\u003c\/p\u003e \u003cp\u003e8.2 Perspective Transformation 269\u003c\/p\u003e \u003cp\u003e8.2.1 Clipping 273\u003c\/p\u003e \u003cp\u003e8.2.2 Interpolating the \u003ci\u003ez \u003c\/i\u003eCoordinate 275\u003c\/p\u003e \u003cp\u003e8.3 Hidden Surfaces 278\u003c\/p\u003e \u003cp\u003e8.3.1 Back Face Culling 281\u003c\/p\u003e \u003cp\u003e8.3.2 Painter’s Algorithm 283\u003c\/p\u003e \u003cp\u003e8.3.3 Z-Buffer 286\u003c\/p\u003e \u003cp\u003e8.4 Ray Tracing 287\u003c\/p\u003e \u003cp\u003e8.4.1 Bounding Volumes 289\u003c\/p\u003e \u003cp\u003e8.4.2 Bounding Boxes 289\u003c\/p\u003e \u003cp\u003e8.4.3 Bounding Spheres 291\u003c\/p\u003e \u003cp\u003e8.5 Complements and Details 293\u003c\/p\u003e \u003cp\u003e8.5.1 Frustum Planes 293\u003c\/p\u003e \u003cp\u003e8.5.2 Axes for Bounding Volumes 294\u003c\/p\u003e \u003cp\u003e8.6 Exercises 297\u003c\/p\u003e \u003cp\u003e8.6.1 Programming Exercises 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Lighting 299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Color Coordinates 299\u003c\/p\u003e \u003cp\u003e9.2 Elementary Lighting Models 303\u003c\/p\u003e \u003cp\u003e9.2.1 Gouraud and Phong Shading 307\u003c\/p\u003e \u003cp\u003e9.2.2 Shadows 311\u003c\/p\u003e \u003cp\u003e9.2.3 BRDFs in Lighting Models 315\u003c\/p\u003e \u003cp\u003e9.3 Global Illumination 319\u003c\/p\u003e \u003cp\u003e9.3.1 Ray Tracing 319\u003c\/p\u003e \u003cp\u003e9.3.2 Radiosity 323\u003c\/p\u003e \u003cp\u003e9.4 Textures 325\u003c\/p\u003e \u003cp\u003e9.4.1 Mapping 325\u003c\/p\u003e \u003cp\u003e9.4.2 Resolution 332\u003c\/p\u003e \u003cp\u003e9.4.3 Procedural Textures 333\u003c\/p\u003e \u003cp\u003e9.5 Complements and Details 335\u003c\/p\u003e \u003cp\u003e9.5.1 Conversion between RGB and HSV 335\u003c\/p\u003e \u003cp\u003e9.5.2 Shadows on Arbitrary Planes 336\u003c\/p\u003e \u003cp\u003e9.5.3 Derivation of the Radiosity Equation 337\u003c\/p\u003e \u003cp\u003e9.6 Exercises 339\u003c\/p\u003e \u003cp\u003e9.6.1 Programming Exercises 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Other Paradigms 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Pixels 342\u003c\/p\u003e \u003cp\u003e10.1.1 Bresenham Line Algorithm 342\u003c\/p\u003e \u003cp\u003e10.1.2 Anti-Aliasing 345\u003c\/p\u003e \u003cp\u003e10.1.3 Compositing 347\u003c\/p\u003e \u003cp\u003e10.2 Noise 350\u003c\/p\u003e \u003cp\u003e10.2.1 Random Number Generation 350\u003c\/p\u003e \u003cp\u003e10.2.2 Distributions 351\u003c\/p\u003e \u003cp\u003e10.2.3 Sequences of Random Numbers 353\u003c\/p\u003e \u003cp\u003e10.2.4 Uniform and Normal Distributions 354\u003c\/p\u003e \u003cp\u003e10.2.5 Terrain Generation 356\u003c\/p\u003e \u003cp\u003e10.2.6 Noise Generation 357\u003c\/p\u003e \u003cp\u003e10.3 L-Systems 361\u003c\/p\u003e \u003cp\u003e10.3.1 Grammars 362\u003c\/p\u003e \u003cp\u003e10.3.2 Turtle Interpretation 363\u003c\/p\u003e \u003cp\u003e10.3.3 Analysis of Grammars 365\u003c\/p\u003e \u003cp\u003e10.3.4 Extending L-Systems 367\u003c\/p\u003e \u003cp\u003e10.4 Exercises 368\u003c\/p\u003e \u003cp\u003e10.4.1 Programming Exercises 369\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Geometry and Trigonometry 370\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Triangles 370\u003c\/p\u003e \u003cp\u003eA.2 Angles 372\u003c\/p\u003e \u003cp\u003eA.3 Trigonometric Functions 373\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Linear Algebra 376\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Systems of Linear Equations 376\u003c\/p\u003e \u003cp\u003eB.1.1 Solving the System 377\u003c\/p\u003e \u003cp\u003eB.2 Matrix Properties 379\u003c\/p\u003e \u003cp\u003eB.3 Vector Spaces 381\u003c\/p\u003e \u003cp\u003eReferences 383\u003c\/p\u003e \u003cp\u003eIndex 387\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406909612375,"sku":"9781118712191","price":57.9,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118712191.jpg?v=1730497526"},{"product_id":"demystifying-deep-learning-9781394205608","title":"Demystifying Deep Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDEMYSTIFYING DEEP LEARNING\u003c\/b\u003e \u003cp\u003e\u003cb\u003eDiscover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! \u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science, for example. Just as the robot revolution threatened blue-collar jobs in the 1970s, so now the AI revolution promises a new era of productivity for white collar jobs. Important tasks have begun being taken over by ANNs, from disease detection and prevention, to reading and supporting legal contracts, to understanding experimental data, model protein folding, and hurricane modeling. AI is everywhereon the news, in think tanks, and occupies government policy makers all over the world and ANNs often provide the backbone for AI. \u003c\/p\u003e\u003cp\u003eRelying on an informal and succinct approach, \u003ci\u003eDemystifying Deep Learni\u003c\/i\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407603999063,"sku":"9781394205608","price":96.3,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781394205608.jpg?v=1730499907"},{"product_id":"math-for-security-from-graphs-and-geometry-to-spatial-analysis-9781718502567","title":"Math For Security: From Graphs and Geometry to","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eApplied Math for Security is one of the first math-based guides specifically geared for information security practitioners. Readers will learn how to use concepts from various fields of mathematics - like graph theory, computational geometry, and statistics - to create and implement ready-to-use security tools. The book is written in a lively, conversational style that engages readers from the get-go. Chapters are enriched with code examples written in Python, and feature hands-on 'proof of concept' projects that involve developing math-based applications to solve real-world problems. Readers are also able to apply the mathematical constructs that they learn to a variety of challenging scenarios, like determining the ideal location for fire stations, disrupting information flow in a social network, building facial recognition software, and designing custom tools for modern security work.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"A very practical book for security. . . . a real eye-opener.\"\u003cbr\u003e\u003cb\u003e—William Gasarch, Professor, University of Maryland-Dept of Computer Science\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\"A really nice introduction to graph theory and computational geometry for people who know a bit of Python and without a mathematical background.\"\u003cbr\u003e\u003cb\u003e—Julien Voisin, Artificial Truth\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\"The book was very easy to follow, I'd expect anyone with a technical or stats background to be able to dive right in given the step-by-step instructions and explanations provided by Daniel.\"\u003cbr\u003e\u003cb\u003e—@WithSandra, tech YouTuber and security analyst\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\"Whether you're an aspiring security professional, a social network analyst, or an innovator seeking to create cutting-edge security solutions, \u003ci\u003eMath for Security\u003c\/i\u003e will empower you to solve complex problems with precision and confidence. \"\u003cbr\u003e\u003cb\u003e—Midwest Book Review\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eAcknowledgments\u003cbr\u003e\u003cbr\u003e Introduction\u003cbr\u003e\u003cb\u003ePART I: ENVIRONMENT AND CONVENTIONS\u003c\/b\u003e\u003cbr\u003eChapter 1: Setting up the Environment\u003cbr\u003eChapter 2: Programming and Math Conventions\u003cbr\u003e\u003cb\u003ePART II: GRAPH THEORY AND COMPUTATIONAL GEOMETRY\u003c\/b\u003e\u003cbr\u003eChapter 3: Securing Networks with Graph Theory\u003cbr\u003eChapter 4: Building a Network Traffic Analysis Tool \u003cbr\u003eChapter 5: Identifying Threats with Social Network Analysis\u003cbr\u003eChapter 6: Analyzing Social Networks to Prevent Security Incidents\u003cbr\u003eChapter 7: Using Geometry to Improve Security Practices\u003cbr\u003eChapter 8: Tracking People in Physical Space with Digital Information\u003cbr\u003eChapter 9: Computational Geometry for Safety Resource Distribution\u003cbr\u003eChapter 10: Computational Geometry for Facial Recognition\u003cbr\u003e\u003cb\u003ePART III: THE ART GALLERY PROBLEM\u003c\/b\u003e\u003cbr\u003eChapter 11: Distributing Security Resources to Guard a Space\u003cbr\u003eChapter 12: The Minimum Viable Product Approach to Security Software Development\u003cbr\u003eChapter 13: Delivering Python Applications\u003cbr\u003eNotes\u003cbr\u003eIndex","brand":"No Starch Press,US","offers":[{"title":"Default Title","offer_id":49411443949911,"sku":"9781718502567","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781718502567.jpg?v=1730513607"},{"product_id":"mathematics-for-modeling-and-scientific-computing-9781848219885","title":"Mathematics for Modeling and Scientific Computing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides the mathematical basis for investigating numerically equations from physics, life sciences or engineering. Tools for analysis and algorithms are confronted to a large set of relevant examples that show the difficulties and the limitations of the most naïve approaches. These examples not only provide the opportunity to put into practice mathematical statements, but modeling issues are also addressed in detail, through the mathematical perspective.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Ordinary Differential Equations 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction to the theory of ordinary differential equations  1\u003c\/p\u003e \u003cp\u003e1.1.1. Existence–uniqueness of first-order ordinary differential equations 1\u003c\/p\u003e \u003cp\u003e1.1.2. The concept of maximal solution  11\u003c\/p\u003e \u003cp\u003e1.1.3. Linear systems with constant coefficients  16\u003c\/p\u003e \u003cp\u003e1.1.4. Higher-order differential equations 20\u003c\/p\u003e \u003cp\u003e1.1.5. Inverse function theorem and implicit function theorem  21\u003c\/p\u003e \u003cp\u003e1.2. Numerical simulation of ordinary differential equations, Euler schemes, notions of convergence, consistence and stability  27\u003c\/p\u003e \u003cp\u003e1.2.1. Introduction  27\u003c\/p\u003e \u003cp\u003e1.2.2. Fundamental notions for the analysis of numerical ODE methods 29\u003c\/p\u003e \u003cp\u003e1.2.3. Analysis of explicit and implicit Euler schemes  33\u003c\/p\u003e \u003cp\u003e1.2.4. Higher-order schemes 50\u003c\/p\u003e \u003cp\u003e1.2.5. Leslie’s equation (Perron–Frobenius theorem, power method)  51\u003c\/p\u003e \u003cp\u003e1.2.6. Modeling red blood cell agglomeration 78\u003c\/p\u003e \u003cp\u003e1.2.7. SEI model 87\u003c\/p\u003e \u003cp\u003e1.2.8. A chemotaxis problem  93\u003c\/p\u003e \u003cp\u003e1.3. Hamiltonian problems 102\u003c\/p\u003e \u003cp\u003e1.3.1. The pendulum problem  106\u003c\/p\u003e \u003cp\u003e1.3.2. Symplectic matrices; symplectic schemes 112\u003c\/p\u003e \u003cp\u003e1.3.3. Kepler problem  125\u003c\/p\u003e \u003cp\u003e1.3.4. Numerical results 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Numerical Simulation of Stationary Partial Differential Equations: Elliptic Problems  141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction  141\u003c\/p\u003e \u003cp\u003e2.1.1. The 1D model problem; elements of modeling and analysis  144\u003c\/p\u003e \u003cp\u003e2.1.2. A radiative transfer problem 155\u003c\/p\u003e \u003cp\u003e2.1.3. Analysis elements for multidimensional problems 163\u003c\/p\u003e \u003cp\u003e2.2. Finite difference approximations to elliptic equations 166\u003c\/p\u003e \u003cp\u003e2.2.1. Finite difference discretization principles  166\u003c\/p\u003e \u003cp\u003e2.2.2. Analysis of the discrete problem 173\u003c\/p\u003e \u003cp\u003e2.3. Finite volume approximation of elliptic equations 180\u003c\/p\u003e \u003cp\u003e2.3.1. Discretization principles for finite volumes 180\u003c\/p\u003e \u003cp\u003e2.3.2. Discontinuous coefficients  187\u003c\/p\u003e \u003cp\u003e2.3.3. Multidimensional problems 189\u003c\/p\u003e \u003cp\u003e2.4. Finite element approximations of elliptic equations  191\u003c\/p\u003e \u003cp\u003e2.4.1. P1 approximation in one dimension 191\u003c\/p\u003e \u003cp\u003e2.4.2. P2 approximations in one dimension  197\u003c\/p\u003e \u003cp\u003e2.4.3. Finite element methods, extension to higher dimensions  200\u003c\/p\u003e \u003cp\u003e2.5. Numerical comparison of FD, FV and FE methods  204\u003c\/p\u003e \u003cp\u003e2.6. Spectral methods  205\u003c\/p\u003e \u003cp\u003e2.7. Poisson–Boltzmann equation; minimization of a convex function, gradient descent algorithm 217\u003c\/p\u003e \u003cp\u003e2.8. Neumann conditions: the optimization perspective  224\u003c\/p\u003e \u003cp\u003e2.9. Charge distribution on a cord 228\u003c\/p\u003e \u003cp\u003e2.10. Stokes problem  235\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Numerical Simulations of Partial Differential Equations: Time-dependent Problems  267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1. Diffusion equations  267\u003c\/p\u003e \u003cp\u003e3.1.1. L2 stability (von Neumann analysis) and L∞ stability: convergence  269\u003c\/p\u003e \u003cp\u003e3.1.2. Implicit schemes  276\u003c\/p\u003e \u003cp\u003e3.1.3. Finite element discretization 281\u003c\/p\u003e \u003cp\u003e3.1.4. Numerical illustrations  283\u003c\/p\u003e \u003cp\u003e3.2. From transport equations towards conservation laws  291\u003c\/p\u003e \u003cp\u003e3.2.1. Introduction  291\u003c\/p\u003e \u003cp\u003e3.2.2. Transport equation: method of characteristics 295\u003c\/p\u003e \u003cp\u003e3.2.3. Upwinding principles: upwind scheme 299\u003c\/p\u003e \u003cp\u003e3.2.4. Linear transport at constant speed; analysis of FD and FV schemes  301\u003c\/p\u003e \u003cp\u003e3.2.5. Two-dimensional simulations  326\u003c\/p\u003e \u003cp\u003e3.2.6. The dynamics of prion proliferation 329\u003c\/p\u003e \u003cp\u003e3.3. Wave equation 345\u003c\/p\u003e \u003cp\u003e3.4. Nonlinear problems: conservation laws 354\u003c\/p\u003e \u003cp\u003e3.4.1. Scalar conservation laws 354\u003c\/p\u003e \u003cp\u003e3.4.2. Systems of conservation laws  387\u003c\/p\u003e \u003cp\u003e3.4.3. Kinetic schemes  393\u003c\/p\u003e \u003cp\u003eAppendices  407\u003c\/p\u003e \u003cp\u003eAppendix 1  409\u003c\/p\u003e \u003cp\u003eAppendix 2  417\u003c\/p\u003e \u003cp\u003eAppendix 3  427\u003c\/p\u003e \u003cp\u003eAppendix 4  433\u003c\/p\u003e \u003cp\u003eAppendix 5  443\u003c\/p\u003e \u003cp\u003eBibliography 447\u003c\/p\u003e \u003cp\u003eIndex  455\u003c\/p\u003e","brand":"ISTE Ltd and John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49413725618519,"sku":"9781848219885","price":125.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781848219885.jpg?v=1730521177"},{"product_id":"geometric-multiplication-of-vectors-an-introduction-to-geometric-algebra-in-physics-9783030017552","title":"Geometric Multiplication of Vectors: An","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book enables the reader to discover elementary concepts of geometric algebra and its applications with lucid and direct explanations. Why would one want to explore geometric algebra? What if there existed a universal mathematical language that allowed one: to make rotations in any dimension with simple formulas, to see spinors or the Pauli matrices and their products, to solve problems of the special theory of relativity in three-dimensional Euclidean space, to formulate quantum mechanics without the imaginary unit, to easily solve difficult problems of electromagnetism, to treat the Kepler problem with the formulas for a harmonic oscillator, to eliminate unintuitive matrices and tensors, to unite many branches of mathematical physics? What if it were possible to use that same framework to generalize the complex numbers or fractals to any dimension, to play with geometry on a computer, as well as to make calculations in robotics, ray-tracing and brain science? In addition, what if such a language provided a clear, geometric interpretation of mathematical objects, even for the imaginary unit in quantum mechanics? Such a mathematical language exists and it is called geometric algebra. High school students have the potential to explore it, and undergraduate students can master it. The universality, the clear geometric interpretation, the power of generalizations to any dimension, the new insights into known theories, and the possibility of computer implementations make geometric algebra a thrilling field to unearth.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eBasic Concepts.- Euclidean 3D Geometric Algebra.- Applications.- Geometric Algebra and Matrices.- Appendix.- Solutions for Some Problems.- Problems.- Why Geometric Algebra?.- Formulae.- Literature.- References.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415612498263,"sku":"9783030017552","price":31.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030017552.jpg?v=1730527513"},{"product_id":"probability-in-electrical-engineering-and-computer-science-an-application-driven-course-9783030499976","title":"Probability in Electrical Engineering and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com.\u003c\/p\u003e  \u003cp\u003eThis is an open access book. \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1. Page Rank - A.- Chapter 2. Page Rank - B.- Chapter 3. Multiplexing - A.- Chapter 4. Multiplexing - B.- Chapter 5. Networks - A.- Chapter 6. Networks - B.- Chapter 7. Digital Link - A.- Chapter 8. Digital Link - B.- Chapter 9. Tracking - A.- Chapter 10. Tracking - B.- Chapter 11. Speech Recognition - A.- Chapter 12. Speech Recognition - B.- Chapter 13. Route planning - A.- Chapter 14. Route Planning - B.- chapter 15. Perspective \u0026amp; Complements.- A. Elementary Probability.- B. Basic Probability.- . Index.\u003cbr\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415622263127,"sku":"9783030499976","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030499976.jpg?v=1730527549"},{"product_id":"computational-diffusion-mri-international-miccai-workshop-lima-peru-october-2020-9783030730178","title":"Computational Diffusion MRI: International MICCAI","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book gathers papers presented at the Workshop on Computational Diffusion MRI, CDMRI 2020, held under the auspices of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), which took place virtually on October 8th, 2020, having originally been planned to take place in Lima, Peru.\u003cp\u003eThis book presents the latest developments in the highly active and rapidly growing field of diffusion MRI. While offering new perspectives on the most recent research challenges in the field, the selected articles also provide a valuable starting point for anyone interested in learning computational techniques for diffusion MRI. The book includes rigorous mathematical derivations, a large number of rich, full-colour visualizations, and clinically relevant results. As such, it is of interest to researchers and practitioners in the fields of computer science, MRI physics, and applied mathematics. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415632322903,"sku":"9783030730178","price":119.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030730178.jpg?v=1730527587"},{"product_id":"the-signed-distance-measure-in-fuzzy-statistical-analysis-theoretical-empirical-and-programming-advances-9783030769154","title":"The Signed Distance Measure in Fuzzy Statistical","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy. The proposed method for estimating these intervals is based on the likelihood method and employs the bootstrap technique. A new metric generalizing the signed distance measure is also developed. In turn, the book presents two conceptually diverse applications in which defended intervals play a role: one is a novel methodology for evaluating linguistic questionnaires developed at the global and individual levels; the other is an extension of the multi-ways analysis of variance to the space of fuzzy sets. To illustrate these approaches, the book presents several empirical and simulation-based studies with synthetic and real data sets. In closing, it presents a coherent R package called “FuzzySTs” which covers all the previously mentioned concepts with full documentation and selected use cases. Given its scope, the book will be of interest to all researchers whose work involves advanced fuzzy statistical methods.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e- 1. Introduction. - \u003cb\u003ePart I Theoretical Part.\u003c\/b\u003e - 2. Fundamental Concepts on Fuzzy Sets. - 3. Fuzzy Rule-Based Systems. - 4. Distances Between Fuzzy Sets. - 5. Fuzzy Random Variables and Fuzzy Distributions. - 6. Fuzzy Statistical Inference. - Conclusion Part I. - \u003cb\u003ePart II Applications.\u003c\/b\u003e - 7. Evaluation of Linguistic Questionnaire. - 8. Fuzzy Analysis of Variance. - \u003cb\u003ePart III An R Package for Fuzzy Statistical Analysis: A Detailed\u003cbr\u003e\u003c\/b\u003e\u003cb\u003eDescription. \u003c\/b\u003e- 9. FuzzySTs: Fuzzy Statistical Tools: A Detailed Description. - Conclusion.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415637172567,"sku":"9783030769154","price":98.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030769154.jpg?v=1730527605"},{"product_id":"computer-algebra-an-algorithm-oriented-introduction-9783030780197","title":"Computer Algebra: An Algorithm-Oriented","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook offers an algorithmic introduction to the field of computer algebra. A leading expert in the field, the author guides readers through numerous hands-on tutorials designed to build practical skills and algorithmic thinking. This implementation-oriented approach equips readers with versatile tools that can be used to enhance studies in mathematical theory, applications, or teaching. Presented using \u003ci\u003eMathematica\u003c\/i\u003e code, the book is fully supported by downloadable sessions in \u003ci\u003eMathematica\u003c\/i\u003e, \u003ci\u003eMaple\u003c\/i\u003e, and \u003ci\u003eMaxima\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003eOpening with an introduction to computer algebra systems and the basics of programming mathematical algorithms, the book goes on to explore integer arithmetic. A chapter on modular arithmetic completes the number-theoretic foundations, which are then applied to coding theory and cryptography. From here, the focus shifts to polynomial arithmetic and algebraic numbers, with modern algorithms allowing the efficient factorization of polynomials. The final chapters offer extensions into more advanced topics: simplification and normal forms, power series, summation formulas, and integration.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eComputer Algebra\u003c\/i\u003e is an indispensable resource for mathematics and computer science students new to the field. Numerous examples illustrate algorithms and their implementation throughout, with online support materials to encourage hands-on exploration. Prerequisites are minimal, with only a knowledge of calculus and linear algebra assumed. In addition to classroom use, the elementary approach and detailed index make this book an ideal reference for algorithms in computer algebra.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“Strong interplay between the abstract exposition, which includes the relevant theorems as well as their proofs, and the practical utilization of those concepts in Mathematica is certainly a remarkable feature of this textbook. … Overall, the book is very well written and the approach to provide examples as actual Mathematica sessions is commendable.” (Andreas Maletti, zbMATH 1484.68004, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415638909271,"sku":"9783030780197","price":42.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030780197.jpg?v=1730527611"},{"product_id":"line-graphs-and-line-digraphs-9783030813840","title":"Line Graphs and Line Digraphs","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIn the present era dominated by computers, graph theory has come into its own as an area of mathematics, prominent for both its theory and its applications. One of the richest and most studied types of graph structures is that of the line graph, where the focus is more on the edges of a graph than on the vertices. \u003cp\u003eA subject worthy of exploration in itself, line graphs are closely connected to other areas of mathematics and computer science. This book is unique in its extensive coverage of many areas of graph theory applicable to line graphs. The book has three parts. Part I covers line graphs and their properties, while Part II looks at features that apply specifically to directed graphs, and Part III presents generalizations and variations of both line graphs and line digraphs.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eLine Graphs and Line Digraphs\u003c\/i\u003e is the first comprehensive monograph on the topic. With minimal prerequisites, the book is accessible to most mathematicians and computer scientists who have had an introduction graph theory, and will be a valuable reference for researchers working in graph theory and related fields.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I Line Graphs.- 1 Fundamentals of Line Graphs.- 2 Line Graph Isomorphisms.- 3 Characterization of Line Graphs.- 4 Spectral Properties of Line Graphs.- 5 Planarity of Line Graphs.- 6 Connectivity of Line Graphs.- 7 Tranversability in Line Graphs.- 8 Colorability in Line Graphs.- 9 Distance and Transitivity in Line Graphs.- Part II Line Digraphs.- 10 Fundamentals of Line Digraphs.- 11 Characterizations of Line Digraphs.- 12 Iterated Line Digraphs.- Part III Generalizations.- 13 Total Graphs and Total Digraphs.- 14 Path Graphs and Path Digraphs.- 15 Super Line Graphs and Super Line Digraphs.- 16 Line Graphs of Signed Graphs.- 17 The Krausz Dimension of Graph.- Reference. Index of Names.- Index of Definitions.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415643496791,"sku":"9783030813840","price":82.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030813840.jpg?v=1730527626"},{"product_id":"cohesive-subgraph-search-over-large-heterogeneous-information-networks-9783030975678","title":"Cohesive Subgraph Search Over Large Heterogeneous","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.\u003c\/p\u003e\u003cp\u003eThe authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas.\u003c\/p\u003e\u003cp\u003eThis SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction2. Preliminaries3. CSS on Bipartite Networks4. CSS on Other General HINs5. Comparison Analysis6. Related Work on CSMs and solutions7. Future Work and Conclusion","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415669514583,"sku":"9783030975678","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030975678.jpg?v=1730527724"},{"product_id":"graph-transformation-15th-international-conference-icgt-2022-held-as-part-of-staf-2022-nantes-france-july-7-8-2022-proceedings-9783031098420","title":"Graph Transformation: 15th International","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the refereed proceedings of the 15th International Conference on Graph Transformation, ICGT 2022, which took place Nantes, France in July 2022.\u003cp\u003eThe 10 full papers and 1 tool paper presented in this book were carefully reviewed and selected from 19 submissions. The conference focuses on describing new unpublished contributions in the theory and applications of graph transformation as well as tool presentation papers that demonstrate main new features and functionalities of graph-based tools.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTheoretical Advances.- Application Domains.- Tool Presentation.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415682195799,"sku":"9783031098420","price":44.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031098420.jpg?v=1730527770"}],"url":"https:\/\/bookcurl.com\/collections\/maths-for-computer-scientists.oembed?page=5","provider":"Book Curl","version":"1.0","type":"link"}