Algorithms and data structures Books

Book SynopsisThis self-contained textbook introduces students and researchers of AI to the key mathematical concepts and techniques necessary to learn and analyze machine learning algorithms. Readers will gain the technical knowledge needed to understand research papers in theoretical machine learning, without much difficulty.Trade Review'This graduate-level text gives a thorough, rigorous and up-to-date treatment of the main mathematical tools that have been developed for the analysis and design of machine learning methods. It is ideal for a graduate class, and the exercises at the end of each chapter make it suitable for self-study. An excellent addition to the literature from one of the leading researchers in this area, it is sure to become a classic.' Peter Bartlett, University of California, Berkeley'This book showcases the breadth and depth of mathematical ideas in learning theory. The author has masterfully synthesized techniques from the many disciplines that have contributed to this subject, and presented them in an accessible format that will be appreciated by both newcomers and experts alike. Readers will learn the tools-of-the-trade needed to make sense of the research literature and to express new ideas with clarity and precision.' Daniel Hsu, Columbia University'Tong Zhang shares in this book his deep and broad knowledge of machine learning, writing an impressively comprehensive and up-to-date reference text, providing a rigorous and rather advanced treatment of the most important topics and approaches in the mathematical study of machine learning. As an authoritative reference and introduction, his book will be a great asset to the field.' Robert Schapire, Microsoft Research'This book gives a systematic treatment of the modern mathematical techniques that are commonly used in the design and analysis of machine learning algorithms. Written by a key contributor to the field, it is a unique resource for graduate students and researchers seeking to gain a deep understanding of the theory of machine learning.' Shai Shalev-Shwartz, Hebrew University of JerusalemTable of Contents1. Introduction; 2. Basic probability inequalities for sums of independent random variables; 3. Uniform convergence and generalization analysis; 4. Empirical covering number analysis and symmetrization; 5. Covering number estimates; 6. Rademacher complexity and concentration inequalities; 7. Algorithmic stability analysis; 8. Model selection; 9. Analysis of kernel methods; 10. Additive and sparse models; 11. Analysis of neural networks; 12. Lower bounds and minimax analysis; 13. Probability inequalities for sequential random variables; 14. Basic concepts of online learning; 15. Online aggregation and second order algorithms; 16. Multi-armed bandits; 17. Contextual bandits; 18. Reinforcement learning; A. Basics of convex analysis; B. f-Divergence of probability measures; References; Author index; Subject index.

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Book SynopsisPreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.Trade Review“This book explains the mathematical foundations of public key cryptography in a mathematically correct and thorough way without omitting important practicalities. … I would like to emphasize that the book is very well written and quite clear. Topics are well motivated, and there are a good number of examples and nicely chosen exercises. To me, this book is still the first-choice introduction to public-key cryptography.” (Klaus Galensa, Computing Reviews, March, 2015)“This is a text for an upper undergraduate/lower graduate course in mathematical cryptography. … It is very well written and quite clear. Topics are well-motivated, and there are a good number of examples and nicely chosen exercises. … An instructor of a fairly sophisticated undergraduate course in cryptography who wants to emphasize public key cryptography should definitely take a look at this book.” (Mark Hunacek, MAA Reviews, October, 2014)Table of ContentsPreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.

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Book SynopsisMaster how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialised script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: 1. An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia; 2. Options for Julia IDEs; 3. Programming structures and functions; 4. Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data pre-processing; 5. Data visualisation and some simple yet powerful statistics for data exploration purposes; 6. Dimensionality reduction and feature evaluation; 7. Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines); 8. Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.

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Book SynopsisMachine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included.Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machiTable of Contents1. Introduction 2. Artificial Neural Networks 3. Fuzzy Logic 4. Support Vector Machine 5. Genetic Algorithm (GA) 6. Hybrid Systems 7. Data Statistics and Analytics 8. Applications in the Civil Engineering Domain 9. Conclusion and Future Scope of Work

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Book SynopsisAutomata and Computability is a class-tested textbook which provides a comprehensive and accessible introduction to the theory of automata and computation. The author uses illustrations, engaging examples, and historical remarks to make the material interesting and relevant for students. It incorporates modern/handy ideas, such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus. The book also shows how to sculpt automata by making the regular language conversion pipeline available through a simple command interface. A Jupyter notebook will accompany the book to feature code, YouTube videos, and other supplements to assist instructors and studentsFeatures Uses illustrations, engaging examples, and historical remarks to make the material accessible Incorporates modern/handy ideas, such as derivative-based parsing and a Lambda reducer showing the universality of Lambda Trade Review"I have taught formal languages and automata theory for decades, and I have seen many, perhaps most, students struggle with the material because it is so abstract. I've often thought that computer science students would learn it better by programming it. Indeed, that's how I really learned these topics -- by implementing constructions directly in practical compiler generation and formal verification tools to do my research. Prof. Gopalakrishnan's approach is to have students learn by doing, while still going into greater depth than some purely pencil-and-paper courses." -Prof. David L. Dill, Donald E. Knuth Professor, Emeritus, in the School of Engineering, Stanford University "It is probably a safe assumption to make these days that many, if not most, computer science undergraduates have had programming experience, but few of them know the language of mathematics. Professor Gopalakrishnan’s book builds on the student’s experience in programming and animates the theory of automata, formal languages, and computability with actual programs which the student can easily modify and play with. Doing is the best way of learning. This book should enable the typical computer science student to acquire a more visceral, and therefore in the long run more useful, understanding of the theory." -Dr. Ching-Tsun Chou, Silicon Architecture Engineer, Intel Corporation "As a long-time researcher in programming languages and high-performance computing, I find the coverage of Automata and Computability in this book illuminating from a foundational perspective as well as timely from a practical perspective. In addition to classical topics such as automata theory and parsing, it allows a student to interactively study via Jupyter notebooks a wide range of topics including grammar disambiguation, Boolean satisfiability, Post Correspondence and Lambda Calculus --- all important topics for students who aspire to become proficient in computer science." -Vivek Sarkar, Professor, School of Computer Science & Stephen Fleming Chair for Telecommunications, College of Computing, Georgia Institute of Technology "I have taught formal languages and automata theory for decades, and I have seen many, perhaps most, students struggle with the material because it is so abstract. I've often thought that computer science students would learn it better by programming it. Indeed, that's how I really learned these topics -- by implementing constructions directly in practical compiler generation and formal verification tools to do my research. Prof. Gopalakrishnan's approach is to have students learn by doing, while still going into greater depth than some purely pencil-and-paper courses." -Prof. David L. Dill, Donald E. Knuth Professor, Emeritus, in the School of Engineering, Stanford University "It is probably a safe assumption to make these days that many, if not most, computer science undergraduates have had programming experience, but few of them know the language of mathematics. Professor Gopalakrishnan’s book builds on the student’s experience in programming and animates the theory of automata, formal languages, and computability with actual programs which the student can easily modify and play with. Doing is the best way of learning. This book should enable the typical computer science student to acquire a more visceral, and therefore in the long run more useful, understanding of the theory." -Dr. Ching-Tsun Chou, Silicon Architecture Engineer, Intel Corporation "As a long-time researcher in programming languages and high-performance computing, I find the coverage of Automata and Computability in this book illuminating from a foundational perspective as well as timely from a practical perspective. In addition to classical topics such as automata theory and parsing, it allows a student to interactively study via Jupyter notebooks a wide range of topics including grammar disambiguation, Boolean satisfiability, Post Correspondence and Lambda Calculus --- all important topics for students who aspire to become proficient in computer science." -Vivek Sarkar, Professor, School of Computer Science & Stephen Fleming Chair for Telecommunications, College of Computing, Georgia Institute of Technology Table of ContentsI Foundations 1 What Machines Think 2 Defining Languages: Patterns in Sets of Strings 3 Kleene Star: Basic Method of defining Repetitious Patterns II Machines 4 Basics of DFAs 5 Designing DFA 6 Operations on DFA 7 Nondeterministic Finite Automata 8 Regular Expressions and NFA 9 NFA to RE conversion 10 Derivative-based Regular Expression Matching 11 Context-Free Languages and Grammars 12 Pushdown Automata 13 Turing Machines III Concepts 14 Interplay Between Formal Languages 15 Post Correspondence, and Other Undecidability Proofs 16 NP-Completeness 17 Binary Decision Diagrams as Minimal DFA 18 Computability using Lambdas

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Book SynopsisMordechai (Moti) Ben-Ari is an Associate Professor in the Department of Science Teaching at the Weizmann Institute of Science in Rehovot, Israel.  He is the author of texts on Ada, concurrent programming, programming languages, and mathematical logic, as well as Just a Theory: Exploring the Nature of Science.  In 2004 he was honored with the ACM/SIGCSE Award for Outstanding Contribution to Computer Science Education.Table of ContentsContents Preface xi 1 What is Concurrent Programming? 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Concurrency as abstract parallelism . . . . . . . . . . . . . . . . 2 1.3 Multitasking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 The terminology of concurrency . . . . . . . . . . . . . . . . . 4 1.5 Multiple computers . . . . . . . . . . . . . . . . . . . . . . . . 5 1.6 The challenge of concurrent programming . . . . . . . . . . . . 5 2 The Concurrent Programming Abstraction 7 2.1 The role of abstraction . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Concurrent execution as interleaving of atomic statements . . . . 8 2.3 Justification of the abstraction . . . . . . . . . . . . . . . . . . . 13 2.4 Arbitrary interleaving . . . . . . . . . . . . . . . . . . . . . . . 17 2.5 Atomic statements . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6 Correctness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.7 Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.8 Machine-code instructions . . . . . . . . . . . . . . . . . . . . . 24 2.9 Volatile and non-atomic variables . . . . . . . . . . . . . . . . . 28 2.10 The BACI concurrency simulator . . . . . . . . . . . . . . . . . 29 2.11 Concurrency in Ada . . . . . . . . . . . . . . . . . . . . . . . . 31 2.12 Concurrency in Java . . . . . . . . . . . . . . . . . . . . . . . . 34 2.13 Writing concurrent programs in Promela . . . . . . . . . . . . . 36 2.14 Supplement: the state diagram for the frog puzzle . . . . . . . . 37 3 The Critical Section Problem 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 The definition of the problem . . . . . . . . . . . . . . . . . . . 45 3.3 First attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.4 Proving correctness with state diagrams . . . . . . . . . . . . . . 49 3.5 Correctness of the first attempt . . . . . . . . . . . . . . . . . . 53 3.6 Second attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.7 Third attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.8 Fourth attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.9 Dekker’s algorithm . . . . . . . . . . . . . . . . . . . . . . . . 60 3.10 Complex atomic statements . . . . . . . . . . . . . . . . . . . . 61 4 Verification of Concurrent Programs 67 4.1 Logical specification of correctness properties . . . . . . . . . . 68 4.2 Inductive proofs of invariants . . . . . . . . . . . . . . . . . . . 69 4.3 Basic concepts of temporal logic . . . . . . . . . . . . . . . . . 72 4.4 Advanced concepts of temporal logic . . . . . . . . . . . . . . . 75 4.5 A deductive proof of Dekker’s algorithm . . . . . . . . . . . . . 79 4.6 Model checking . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.7 Spin and the Promela modeling language . . . . . . . . . . . . . 83 4.8 Correctness specifications in Spin . . . . . . . . . . . . . . . . . 86 4.9 Choosing a verification technique . . . . . . . . . . . . . . . . . 88 5 Advanced Algorithms for the Critical Section Problem 93 5.1 The bakery algorithm . . . . . . . . . . . . . . . . . . . . . . . 93 5.2 The bakery algorithm for N processes . . . . . . . . . . . . . . 95 5.3 Less restrictive models of concurrency . . . . . . . . . . . . . . 96 5.4 Fast algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5 Implementations in Promela . . . . . . . . . . . . . . . . . . . . 104

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Book SynopsisLearn with confidence with this hands-on undergraduate textbook for CS2 courses. Active-learning and real-world projects underpin each chapter, briefly reviewing programming fundamentals then progressing to core data structures and algorithms topics including recursion, lists, stacks, trees, graphs, sorting, and complexity analysis. Creative projects and applications put theoretical concepts into practice, helping students master the fundamentals. Dedicated project chapters supply further programming practice using real-world, interdisciplinary problems which students can showcase in their own online portfolios. Example Interview Questions sections prepare students for job applications. The pedagogy supports self-directed and skills-based learning with over 250 ''Try It Yourself'' boxes, many with solutions provided, and over 500 progressively challenging end-of-chapter questions. Written in a clear and engaging style, this textbook is a complete resource for teaching the fundamental skills that today''s students need. Instructor resources are available online, including a test bank, solutions manual, and sample code.

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Book SynopsisArtificial Intelligence has a real impact on our lives and on our environment, and the Sustainable Development Goals enable us to evaluate these impacts in a systematic manner. AI for the Sustainable Development Goals shows how AI potentially affects all SDGs. Positively, but also negatively.Trade Review"Artificial Intelligence is continuously presented as the technology that will help solve some of the most complex problems of contemporary society, including the fight against climate change and global warming. However, reducing modern problems to mere engineering solutions is not straightforward and may have adverse consequences too. In this book, Sætra shows in an elegant and simple way how AI can indeed be a potential solution to some of the most pressing issues humanity is facing today. Yet, it also alerts about the dangers AI may entail without appropriate oversight and highlights the role and responsibilities of those wielding influence over such great powers. An excellent read and a stepping stone towards having AI serving the Sustainable Development Goals." -- Dr. Eduard Fosch-Villaronga, Assistant Professor, eLaw Center for Law and Digital Technologies, Leiden University.Table of ContentsAuthor. 1 Introduction. 2 AI and the SDGs in Context. 3 The Challenge of Evaluating AI Impact. 4 Sustainable Economic Development. 5 Sustainable Social Development. 6 Sustainable Environmental Development. 7 Assessing the Overall Impact of AI. 8 Conclusion. References. Index.

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Book SynopsisToday, Internet of Things (IoT) is ubiquitous as it is applied in practice in everything from Industrial Control Systems (ICS) to e-Health, e-commerce, Cyber Physical Systems (CPS), smart cities, smart parking, healthcare, supply chain management and many more. Numerous industries, academics, alliances and standardization organizations make an effort on IoT standardization, innovation and development. But there is still a need for a comprehensive framework with integrated standards under one IoT vision. Furthermore, the existing IoT systems are vulnerable to huge range of malicious attacks owing to the massive numbers of deployed IoT systems, inadequate data security standards and the resource-constrained nature. Existing security solutions are insufficient and therefore it is necessary to enable the IoT devices to dynamically counter the threats and save the system.Apart from illustrating the diversified IoT applications, this book also addresses the issue of data safekeepinTable of Contents1. IoT Conceptual Model and Application. 2. Standardization of IoT Ecosystems: Open Challenges, Current Solutions, and Future Directions. 3. A Node Reduction Technique for Trojan Detection and Diagnosis in IoT Hardware Devices. 4. Deep-Learning-Empowered Edge Computing-Based IoT Frameworks. 5. A Geo-Referenced Data Collection Microservice Based on IoT Protocols for Smart HazMat Transportation. 6. Impact of Dimentionality Reduction on Performance of IoT Intrusion Detection System. 7. IoT-Based Resources Management and Monitoring for a Smart City. 8. Internet of Things Applications in Marketing. 9. Internet of Things (IoT) for Sustainable Smart Cities. 10. An Integration of IOT and Machine Learning in Smart City Planning. 11. The Internet of Medical Things for Monitoring Health. 12. Secured Multimedia and IoT in Healthcare Computing Paradigms. 13. Designing Contactless Automated Systems Using IoT, Sensors and Artificial Intelligence to Mitigate COVID-19. 14. Analysis Of the Framework for the Development, Security and Efficacy Of IoT-Based Mobile Health-Care Solutions for Antenatal Care.

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Book SynopsisMachine Learning for Criminology and Crime Research: At the Crossroads reviews the roots of the intersection between machine learning, artificial intelligence (AI), and research on crime; examines the current state of the art in this area of scholarly inquiry; and discusses future perspectives that may emerge from this relationship.As machine learning and AI approaches become increasingly pervasive, it is critical for criminology and crime research to reflect on the ways in which these paradigms could reshape the study of crime. In response, this book seeks to stimulate this discussion. The opening part is framed through a historical lens, with the first chapter dedicated to the origins of the relationship between AI and research on crime, refuting the novelty narrative that often surrounds this debate. The second presents a compact overview of the history of AI, further providing a nontechnical primer on machine learning. The following chapter reviews some of the moTable of ContentsChapter 1: The "Novelty Narrative": An Unorthodox IntroductionChapter 2: A Collective Journey: A Short Overview on Artificial IntelligenceChapter 3: Criminology at the Crossroads? Computational PerspectivesChapter 4: To Reframe and Reform: Increasing the Positive Social Impact of Algorithmic Applications in Research on CrimeChapter 5: Causal Inference in Criminology and Crime Research and the Promises of Machine LearningChapter 6: Concluding Remarks

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Book SynopsisAdvancements in the technology and availability of data sources have led to the `Big Data'' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems.Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods.Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key reTable of ContentsList of FiguresList of TablesPreface Section I BACKGROUND Introduction1.1 LARGE-SCALE MACHINE LEARNING 1.2 OPTIMIZATION PROBLEMS 1.3 LINEAR CLASSIFICATION1.3.1 Support Vector Machine (SVM) 1.3.2 Logistic Regression 1.3.3 First and Second Order Methods1.3.3.1 First Order Methods 1.3.3.2 Second Order Methods 1.4 STOCHASTIC APPROXIMATION APPROACH 1.5 COORDINATE DESCENT APPROACH 1.6 DATASETS 1.7 ORGANIZATION OF BOOK Optimisation Problem, Solvers, Challenges and Research Directions2.1 INTRODUCTION 2.1.1 Contributions 2.2 LITERATURE 2.3 PROBLEM FORMULATIONS 2.3.1 Hard Margin SVM (1992) 2.3.2 Soft Margin SVM (1995) 2.3.3 One-versus-Rest (1998) 2.3.4 One-versus-One (1999) 2.3.5 Least Squares SVM (1999) 2.3.6 v-SVM (2000) 2.3.7 Smooth SVM (2001) 2.3.8 Proximal SVM (2001) 2.3.9 Crammer Singer SVM (2002) 2.3.10 Ev-SVM (2003) 2.3.11 Twin SVM (2007) 2.3.12 Capped lp-norm SVM (2017) 2.4 PROBLEM SOLVERS 2.4.1 Exact Line Search Method 2.4.2 Backtracking Line Search 2.4.3 Constant Step Size 2.4.4 Lipschitz & Strong Convexity Constants 2.4.5 Trust Region Method 2.4.6 Gradient Descent Method 2.4.7 Newton Method 2.4.8 Gauss-Newton Method 2.4.9 Levenberg-Marquardt Method 2.4.10 Quasi-Newton Method 2.4.11 Subgradient Method 2.4.12 Conjugate Gradient Method 2.4.13 Truncated Newton Method 2.4.14 Proximal Gradient Method 2.4.15 Recent Algorithms 2.5 COMPARATIVE STUDY 2.5.1 Results from Literature 2.5.2 Results from Experimental Study 2.5.2.1 Experimental Setup and Implementation Details 2.5.2.2 Results and Discussions 2.6 CURRENT CHALLENGES AND RESEARCH DIRECTIONS 2.6.1 Big Data Challenge 2.6.2 Areas of Improvement 2.6.2.1 Problem Formulations 2.6.2.2 Problem Solvers 2.6.2.3 Problem Solving Strategies/Approaches 2.6.2.4 Platforms/Frameworks 2.6.3 Research Directions 2.6.3.1 Stochastic Approximation Algorithms 2.6.3.2 Coordinate Descent Algorithms 2.6.3.3 Proximal Algorithms 2.6.3.4 Parallel/Distributed Algorithms 2.6.3.5 Hybrid Algorithms 2.7 CONCLUSION Section II FIRST ORDER METHODSMini-batch and Block-coordinate Approach 3.1 INTRODUCTION 3.1.1 Motivation 3.1.2 Batch Block Optimization Framework (BBOF) 3.1.3 Brief Literature Review 3.1.4 Contributions 3.2 STOCHASTIC AVERAGE ADJUSTED GRADIENT (SAAG) METHODS3.3 ANALYSIS 3.4 NUMERICAL EXPERIMENTS 3.4.1 Experimental setup 3.4.2 Convergence against epochs 3.4.3 Convergence against Time 3.5 CONCLUSION AND FUTURE SCOPE Variance Reduction Methods 4.1 INTRODUCTION 4.1.1 Optimization Problem 4.1.2 Solution Techniques for Optimization Problem 4.1.3 Contributions 4.2 NOTATIONS AND RELATED WORK 4.2.1 Notations 4.2.2 Related Work 4.3 SAAG-I, II AND PROXIMAL EXTENSIONS 4.4 SAAG-III AND IV ALGORITHMS 4.5 ANALYSIS 4.6 EXPERIMENTAL RESULTS 4.6.1 Experimental Setup 4.6.2 Results with Smooth Problem 4.6.3 Results with non-smooth Problem 4.6.4 Mini-batch Block-coordinate versus mini-batch setting 4.6.5 Results with SVM 4.7 CONCLUSION Learning and Data Access 5.1 INTRODUCTION 5.1.1 Optimization Problem 5.1.2 Literature Review 5.1.3 Contributions 5.2 SYSTEMATIC SAMPLING 5.2.1 Definitions 5.2.2 Learning using Systematic Sampling 5.3 ANALYSIS 5.4 EXPERIMENTS 5.4.1 Experimental Setup 5.4.2 Implementation Details 5.4.3 Results 5.5 CONCLUSION Section III SECOND ORDER METHODS Mini-batch Block-coordinate Newton Method 6.1 INTRODUCTION 6.1.1 Contributions 6.2 MBN 6.3 EXPERIMENTS 6.3.1 Experimental Setup 6.3.2 Comparative Study 6.4 CONCLUSION Stochastic Trust Region Inexact Newton Method 7.1 INTRODUCTION 7.1.1 Optimization Problem 7.1.2 Solution Techniques 7.1.3 Contributions 7.2 LITERATURE REVIEW 7.3 TRUST REGION INEXACT NEWTON METHOD 7.3.1 Inexact Newton Method 7.3.2 Trust Region Inexact Newton Method 7.4 STRON 7.4.1 Complexity 7.4.2 Analysis 7.5 EXPERIMENTAL RESULTS 7.5.1 Experimental Setup 7.5.2 Comparative Study 7.5.3 Results with SVM 7.6 EXTENSIONS 7.6.1 PCG Subproblem Solver 17.6.2 Stochastic Variance Reduced Trust Region Inexact Newton Method 7.7 CONCLUSION Section IV CONCLUSIONConclusion and Future Scope 8.1 FUTURE SCOPE 142 Bibliography Index

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Book SynopsisThis is a how-to book for solving geometric problems robustly or error free in actual practice. The contents and accompanying source code are based on the feature requests and feedback received from industry professionals and academics who want both the descriptions and source code for implementations of geometric algorithms. The book provides a framework for geometric computing using several arithmetic systems and describes how to select the appropriate system for the problem at hand. Key Features: A framework of arithmetic systems that can be applied to many geometric algorithms to obtain robust or error-free implementations Detailed derivations for algorithms that lead to implementable code Teaching the readers how to use the book concepts in deriving algorithms in their fields of application The Geometric Tools Library, a repository of well-tested code at the Geometric Tools website, https:/Table of Contents1.Introduction. 2. Arbitrary Precision Arithmetic. 3. Interval Arithmetic. 4. Computational Geometry Algorithms. 5. Distance Queried. 6. Intersection Queries. 7. Mixed-Mode Computing. 8. Robust Floating-Point Computing. 9. Implementation of Arithmetic

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Book SynopsisAuthored by two standout professors in the field of Computer Science and Technology with extensive experience in instructing, Learn Programming with C: An Easy Step-by Step Self-Practice Book for Learning C is a comprehensive and accessible guide to programming with one of the most popular languages.Meticulously illustrated with figures and examples, this book is a comprehensive guide to writing, editing, and executing C programs on different operating systems and platforms, as well as how to embed C programs into other applications and how to create oneâs own library. A variety of questions and exercises are included in each chapter to test the readersâ knowledge.Written for the novice C programmer, especially undergraduate and graduate students, this bookâs line-by-line explanation of code and succinct writing style makes it an excellent companion for classroom teaching, learning, and programming labs.Table of ContentsPrefaceChapter 1: Introduction History of Programming Language Different Types of Programming Language Importance of Programming C Program Structure Step-by-Step Tutorial to Run a C Program Keywords Identifiers Operators Operator Precedence in C Variables Constants Escape Sequences Data Types Type Casting Examples Exercises MCQ with Answers Questions with Short Answers Problems to Practice Chapter 2: Flow-Control if Statement if..else Statement Nested if..else Statement Conditional Operator for Loop while Loop do..while Loop continue Statement break Statement switch..case Statement goto Statement Examples Exercises MCQ with Answers Questions with Short Answers Problems to Practice Chapter 3: Arrays and Pointers Arrays 2-D arrays Multidimensional arrays String String Function Pointers Memory Allocation Examples Exercises MCQ with Answers Questions with Short Answers Problems to Practice Chapter 4: Functions Function Types Function Structure Function Call Arrays and Functions Pointers and Functions Storage Class Examples Exercises MCQ with Answers Questions with Short Answers Problems to Practice Chapter 5: Structure and Union Structure Union enum Data Structure and Algorithm Linked List Types of Linked List Examples Exercises MCQ with Answers Questions with Short Answers Problems to Practice Chapter 6: File Management File Types File Operations Preprocessors Conditional Compilation Examples Exercises MCQ with Answers Questions with Short Answers Problems to Practice Chapter 7: C Graphics Introduction Functions Color Table Fonts of Text Fill Patterns Including graphics.h in CodeBlocks Examples Problems to PracticeChapter 8: C Cross-platform Creating Own Library Turbo C Visual Studio Code Visual Studio Command Line Command Line Arguments Linux Embedding C Code into MATLAB Integrating C Code into Python Switching from One Language to Another Transition to C++ or C# from C Chapter 9: C Projects

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Book SynopsisThis book focuses on the values and effects that are operational in data technologies as they sustain colonial and imperialist legacies while also highlighting strategies for resistance to autocratic regimes and pathways towards decolonizing efforts.Systems and schemes for databases and automated data flow processing often contain implicitly Westernized, autocratic or even imperialist features, but can also be appropriated for resistance and revolt. Algorithms are not strictly mathematical but also embody cultural constructs. Values circulate in systems along with labels and quantities. This entails more critically reflective data practices whether in government, academia, industry or the civic sphere. The volume covers a critique of the data colonialism thesis which frames computer science as a colonizing science that uses data to classify and govern us, an alternate framing of metadata as data near data' to challenge seemingly neutral technical terms, and a case study of thTable of Contents1 Notes on the Historiography of Data Colonialism; 2 Metadata Is Not Data About Data; 3 Social Media Use in the Sudanese Uprising, 2018: Mediating Civilian–Military Discourse

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Algorithms: Technology, Culture, Politics develops a relational, situated approach to algorithms. It takes a middle ground between theories that give the algorithm a singular and stable meaning in using it as a central analytic category for contemporary society and theories that dissolve the term into the details of empirical studies.The book discusses algorithms in relation to hardware and material conditions, code, data, and subjects such as users, programmers, but also data doubles. The individual chapters bridge critical discussions on bias, exclusion, or responsibility with the necessary detail on the contemporary state of information technology. The examples include state-of-the-art applications of machine learning, such as self-driving cars, and large language models such as GPT.The book will be of interest for everyone engaging critically with algorithms, particularly in the social sciences, media studies, STS, political theory, or philosophy. With its b

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Book SynopsisThe book Advances in Distance Learning in Times of Pandemic is devoted to the issues and challenges faced by universities in the field of distance learning in COVID-19 times. It covers both the theoretical and practical aspects connected to distance education. It elaborates on issues regarding distance learning, its challenges, assessment by students and their expectations, the use of tools to improve distance learning, and the functioning of e-learning in the industry 4.0 and society 5.0 eras. The book also devotes a lot of space to the issues of Web 3.0 in university e-learning, quality assurance, and knowledge management. The aim and scope of this book is to draw a holistic picture of ongoing online teaching-activities before and during the lockdown period and present the meaning and future of e-learning from studentsâ points of view, taking into consideration their attitudes and expectations as well as industry 4.0 and society 5.0 aspects. The book presents the approach to distance learning and how it has changed, especially during a pandemic that revolutionized education. It highlights â the function of online education and how that has changed before and during the pandemic. â how e-learning is beneficial in promoting digital citizenship. â distance learning characteristic in the era of industry 4.0 and society 5.0. â how the era of industry 4.0 treats distance learning as a desirable form of education. The book covers both scientific and educational aspects and can be useful for university-level undergraduate, postgraduate and research-grade courses and can be referred to by anyone interested in exploring the diverse aspects of distance learning.

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Book SynopsisTurn your love of video games into a new love of programming by learning the ins and outs of writing code while also learning how to keep track of high scores, what video game heroes and loot boxes are made of, how the dreaded RNG (random number generation) works, and much, much more. This book is the first in an ongoing series designed to take readers from no coding knowledge to writing their own video games and interactive digital experiences using industry standard languages and tools. But coding books are technical, boring, and scary, arenât they? Not this one. Within these pages, readers will find a fun and approachable adventure that will introduce them to the essential programming fundamentals like variables, computer-based math operations, RNG, logic structures, including if-statements and loops, and even some object-oriented programming. Using Visual Studio and C#, readers will write simple but fun console programs and text-based games that will build coding skills a

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Book SynopsisMachine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields.This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers.All codes are available on the author''s website: CLab (nau.edu)They areTable of ContentsChapter 1: Multivariate Calculus. Chapter 2: Probability Theory. Chapter 3: Dimensionality Reduction. Chapter 4: Cluster Analysis. Chapter 5: Vector Quantization Techniques. Chapter 6: Regression Models. Chapter 7: Classification. Chapter 8: Feedforward Networks. Chapter 9: Advanced Network Architectures. Chapter 10: Value Methods. Chapter 11: Gradient Methods. Chapter 12: Population-Based Metaheuristic Methods. Chapter 13: Local Search methods. Appendix A: Sufficient Statistic. Appendix B: Graphs. Appendix C: Sequential Minimization Optimization. Appendix D: Algorithmic Differentiation. Appendix E: Batch Normalizing Transform. Appendix F: Divergence of Two Gaussian Distributions. Appendix G: Continuous-time Bellman's Equation. Appendix H: Conjugate Gradient. Appendix I: Importance Sampling. References. Index.

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Book SynopsisNew Perspectives in Behavioral Cybersecurity offers direction for readers in areas related to human behavior and cybersecurity, by exploring some of the new ideas and approaches in this subject, specifically with new techniques in this field coming from scholars with very diverse backgrounds in dealing with these issues. It seeks to show an understanding of motivation, personality, and other behavioral approaches to understand cyberattacks and create cyberdefenses.This book:â Elaborates cybersecurity concerns in the work environment and cybersecurity threats to individuals. â Presents personality characteristics of cybersecurity attackers, cybersecurity behavior, and behavioral interventions. â Highlights the applications of behavioral economics to cybersecurity. â Captures the management and security of financial data through integrated software solutions. â Examines the importance of studying fake news proliferation by detecting cTable of ContentsSection I. Cybersecurity Concerns in the Work Environment. 1. Management and Security of Financial Data Through Integrated Software Solutions. 2. An Efficient Scheme For Detecting And Mitigating Insider Threats. 3. (Figures query) Phishing Through URLs: An Instance Based Learning Model Approach to Detecting Phishing. Section II. Cybersecurity Threats to the Individual. 4. Video Games in Digital Forensics. 5. Dances with the Illuminati: Hands-On Social Engineering in Classroom Setting. 6. Studying Fake News Proliferation by Detecting Coordinated Inauthentic Behavior. 7. Refining the Sweeney Approach on Data Privacy. Section III. Cybersecurity Concerns in the Home and Work Environment. 8. Cybersecurity Hygiene: Blending Home and Work Computing. 9. Will a Cybersecurity Mindset shift build and sustain a Cybersecurity Pipeline?. Section IV. Ethical Behavior. 10. Cybersecurity Behavior and Behavioral Interventions. Section V. Differences in Languages in Cyberattacks. 11. Using Language Differences to Detect Cyberattacks: Ukrainian and Russian. Section VI. Applications of Behavioral Economics to Cybersecurity. 12. Using Economic Prospect Theory To Quantify Side Channel Attacks. 13. A Game-Theoretic Approach to Detecting Romance Scams. Section VII. New Approaches for Future Research. 14. (Unfinished?) Human-Centered Artificial intelligence: Threats and Opportunities for Cybersecurity.

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Book SynopsisThe last two decades have seen a wave of exciting new developments in the theory of algorithmic randomness and its applications to other areas of mathematics. This volume surveys much of the recent work that has not been included in published volumes until now. It contains a range of articles on algorithmic randomness and its interactions with closely related topics such as computability theory and computational complexity, as well as wider applications in areas of mathematics including analysis, probability, and ergodic theory. In addition to being an indispensable reference for researchers in algorithmic randomness, the unified view of the theory presented here makes this an excellent entry point for graduate students and other newcomers to the field.Table of Contents1. Key developments in algorithmic randomness Johanna N. Y. Franklin and Christopher P. Porter; 2. Algorithmic randomness in ergodic theory Henry Towsner; 3. Algorithmic randomness and constructive/computable measure theory Jason Rute; 4. Algorithmic randomness and layerwise computability Mathieu Hoyrup; 5. Relativization in randomness Johanna N. Y. Franklin; 6. Aspects of Chaitin's Omega George Barmpalias; 7. Biased algorithmic randomness Christopher P. Porter; 8. Higher randomness Benoit Monin; 9. Resource bounded randomness and its applications Donald M. Stull; Index.

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Book SynopsisAimed at graduate students and researchers, this book offers a model-driven approach to the study and manipulation of dynamical systems. Based on an online course hosted by the Complexity Explorer, it uses analytical tools from information theory and complexity science to tackle key challenges in network and systems biology.Table of ContentsIntroduction; Part I. Preliminaries: 1. A computational approach to causality; 2. Networks: from structure to dynamics; 3. Information and computability theories; Part II. Theory and Methods: 4. Algorithmic information theory; 5. The coding theorem method (CTM); 6. The block decomposition method (BDM); 7. Graph and tensor complexity; 8. Algorithmic information dynamics (AID); Part III. Applications: 9. From theory to practice; 10. Algorithmic dynamics in artificial environments; 11. Applications to integer and behavioural sequences; 12. Applications to evolutionary biology; Postface; Appendix: Mutual and conditional BDM; Glossary.

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Book SynopsisThis text provides a hands-on introduction to writing software in Python, with no prior programming experience required. It offers sections designed for approximately one class period each, and proceeds gradually from procedural to object-oriented design. Examples, exercises, and projects are included from diverse application domains.Table of Contents1. Turtle Graphics 2. Numeric Data 3. Text 4. Images 5. Objects and Classes

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Book SynopsisThis book gives students a quick and accessible introduction to many of the most important concepts in the field of algorithmic game theory. It demonstrates these concepts through case studies in online advertising, wireless spectrum auctions, kidney exchange, and network management.Trade Review'There are several features of this book that make it very well suited both for the classroom and for self-study … if your interest is in understanding how game theory, economics and computer science are cross-pollinating to address challenges of the design of online strategic interactions, this is the book to start with. It is clear, well-organized and makes a compelling introduction to a vibrant field.' David Burke, MAA ReviewsTable of Contents1. Introduction and examples; 2. Mechanism design basics; 3. Myerson's Lemma; 4. Algorithmic mechanism design 34; 5. Revenue-maximizing auctions; 6. Simple near-optimal auctions; 7. Multi-parameter mechanism design; 8. Spectrum auctions; 9. Mechanism design with payment constraints; 10. Kidney exchange and stable matching; 11. Selfish routing and the price of anarchy; 12. Network over-provisioning and atomic selfish routing; 13. Equilibria: definitions, examples, and existence; 14. Robust price-of-anarchy bounds in smooth games; 15. Best-case and strong Nash equilibria; 16. Best-response dynamics; 17. No-regret dynamics; 18. Swap regret and the Minimax theorem; 19. Pure Nash equilibria and PLS-completeness; 20. Mixed Nash equilibria and PPAD-completeness.

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Book SynopsisEntity Framework 6 Recipes provides an exhaustive collection of ready-to-use code solutions for Entity Framework, Microsoft's model-centric, data-access platform for the .NET Framework and ASP.NET development.Table of Contents Getting Started with Entity Framework Entity Data Modeling Fundamentals Querying an Entity Data Model Using Entity Framework in ASP.NET Loading Entities and Navigation Properties Beyond the Basics with Modeling and Inheritance Working with Object Services Plain Old CLR Objects Using the Entity Framework in N-Tier Applications Stored Procedures Functions Customizing Entity Framework Objects Improving Performance Concurrency

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Book Synopsis Human attention is in the highest demand it has ever been. The drastic increase in available information has compelled individuals to find a way to sift through the media that is literally at their fingertips. Content recommendation systems have emerged as the technological solution to this social and informational problem, but they''ve also created a bigger crisis in confirming our biases by showing us only, and exactly, what it predicts we want to see.   Data versus Democracy investigates and explores how, in the era of social media, human cognition, algorithmic recommendation systems, and human psychology are all working together to reinforce (and exaggerate) human bias. The dangerous confluence of these factors is driving media narratives, influencing opinions, and possibly changing election results.  In this book, algorithmic recommendations, clickbait, familiarity bias, propaganda, Trade Review“A very well written book that has an engaging style of writing, doesn’t become dry or bogged down in the details, but still showcases the depth of knowledge that Shaffer has on the subject. … It’s accessible and it provides a satisfying read to those looking for deep analysis of this emerging problem faced by the world.” (The Robotics Law Journal, Vol. 5 (2), September - October, 2019) Table of ContentsPart I: The Propaganda Problem.- Chapter 1: Pay Attention: How Information Abundance Affects the Way We Consume Media .- Chapter 2: Cog in the System: How the Limits of Our Brains Leave Us Vulnerable to Cognitive Hacking.- Chapter 3: Swimming Upstream: How Content Recommendation Engines Impact Information and Manipulate Our Attention.- Part II: Case Studies.- Chapter 4: Domestic Disturbance: Ferguson, GamerGate, and the Rise of the American Alt-Right.- Chapter 5: Democracy Hacked, Part 1: Russian Interference and the New Cold War .- Chapter 6: Democracy Hacked, Part 2: Rumors, Bots, and Genocide in the Global South .- Chapter 7: Conclusion: Where Do We Go from Here?.-

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Book Synopsis Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You''ll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You''ll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning.  Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analyticTable of Contents CHAPTER 1: Overview of Python Language 1.1 Philosophy of Python programming 1.2 Comparison with other languages 1.4 Design patterns in Python 1.4.1 Structural patterns 1.4.2 Behavioral patterns 1.4.3 Creational patterns 1.5 Why Python is so popular? 1.6 Use-case where Python does not fit well 1.7 Interfacing Python with other languages 1.7.1 Running Stanford NLP Java library in Python 1.7.2 Running time series Holt- Winter R module in Python 1.7.3 Expose your Python program as service in 2 minutes 1.8 Essential architectural pattern in data analytics 1. Hot Potato anti pattern 2. Data collector as a service 3. Bridge & proxy patterns. 4. Application layering CHAPTER 2: ETL with Python 2.1 Introduction 2.2 Python &Mysql 2.3 Python & Neo4j 2.4 Python & Elastic Search 2.5 Crawling with Beautiful Soup 2.6 Crawling using selenium 2.7 Regular expressions 2.8 Panda framework 2.9 Cloud Storages 2.9.1 AWS storage 2.10.1 GCP storages 2.9 Topical crawling 2.9.1 Find potential activists for a political party from web CHAPTER 3: Supervised Learning and Unsupervised Learning with Python 3.1. Introduction 3.2 Correlation analysis 3.2.1 Measures of correlation 3.2.2 Threshold for correlation 3.2.3 Dealing uneven cordiality of features 3.3 Principle component analysis 3.3.1 Singular value decomposition algorithm 3. 3.2 Factor analysis 3.3.3 Use case: Measuring impact of change in organization 3.4 Mutual information & dealing with categorical data 3.4.1 Use case: Measuring most significant features in ad price prediction 3.5 Feature engineering in texts and images 3.5.1 Classification 3. 5.2 Decision tree & entropy gain 3. 5.3 Random forest classifier 3. 5.4 Naïve bay’s classifier 3. 5.5 Support vector machine 3. 5.6 Text classification using Python 3. 5.7 Image classification using Python 3. 5.8 Supervised & unsupervised learning 3. 5.9. Semi supervised learning 3. 6.1 Regression 3. 6.2 Least-square estimation 3. 6.3 Logistic regression 3. 6.4 Classification using regression 3.6.5 Feature scaling 3.6.6 Intentionally bias the model to over fit or under fit CHAPTER 4: Clustering with Python 4.1 Introduction 4.2 Distance measures 4.3 Hierarchical clustering 4.3.1 Top to bottom algorithm 4.3.2 Bottom to top algorithm 4.3.3 Dendrogram to cluster 4.3.4 Choosing the threshold 4.4 K-Mean clustering 4.4.1 Algorithm 4.4.2 Choosing K 4.5 Graph theoretic approach 4.6 Measure for good clustering 4.7 Find summary of a paragraph 4.8 Find faces in images CHAPTER 5: Deep Learning & Neural Networks 5.1 History 5.2 Architecture 5.3 Use-case where NN fit well 5.4 Back propagation algorithm 5.5 Quick tour to other NN algorithms 5.6 Regularization techniques 5.7 Recurrent neural network 5.8 Goal oriented dialog system 5. 9.1 Convolution neural network 5. 9.2 Fake image detection Introduction to reinforcement learning 1. Dancing Floor on GCP 2. Dialectic Learning CHAPTER 6: Time Series Analysis 6.1 Introduction 6.2 Smoothing techniques 6.3 Autoregressive model 6.4 Moving average model 6.5 ARMA model 6.6 ARIMA model 6.7. SARIMA model 6.8 Historical practice 6.9 Frequency domain analysis in time series CHAPTER 7: Analytics in Scale 7.1 Introduction 7.2 Hadoop architecture 7.3 Popular design pattern in MapReduce 7.4 Introduction to cloud 7.5. Analytics on cloud 7.6 Introduction to Spark 7.7. Spark architecture - Memory optimization - Problem with memory optimization - Essential parameter in Spark - Naïve Bayes classifier in Spark 7.8 A recommendation system in Spark

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Book SynopsisGenerating functions, one of the most important tools in enumerative combinatorics, are a bridge between discrete mathematics and continuous analysis. Generating functions have numerous applications in mathematics, especially in - Combinatorics - Probability Theory - Statistics - Theory of Markov Chains - Number Theory One of the most important and relevant recent applications of combinatorics lies in the development of Internet search engines whose incredible capabilities dazzle even the mathematically trained user.Trade Review" ""Wilf's writing is clear and friendly; his exorcises are instructive and plentiful... This book is valuable reading for even the best of specialists..."" -E. Rodney Canfield, The Mathematical Intelligencer , March 1993 ""This is a first rate, carefully planned and executed book written by a 'black belt gereratingfunctionologist.' I'll be using it the next time I teach..."" -George Andrews, SIAM News, October 1994 ""Wilf's book is very well-written and easy to read by any serious mathematics student. Scientists in other disciplines often encounter the need to study sequences that naturally arise in their own discipline. The book is well-suited fo them, too."" -Short Book Reviews, January 2006"Table of ContentsIntroductory Ideas and Examples. Series. Cards, Decks and Hands: The Exponential Formula. Applications of Generating Functions. Analytic and Asymptotic Models. Appendix: Using Maple and Mathematica Solutions. References.

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Book SynopsisAs one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.Trade Review"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms." < /br>Fernando Berzal, Computing ReviewsTable of ContentsPrologue: a machine learning sampler; 1. The ingredients of machine learning; 2. Binary classification and related tasks; 3. Beyond binary classification; 4. Concept learning; 5. Tree models; 6. Rule models; 7. Linear models; 8. Distance-based models; 9. Probabilistic models; 10. Features; 11. In brief: model ensembles; 12. In brief: machine learning experiments; Epilogue: where to go from here; Important points to remember; Bibliography; Index.

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Book SynopsisA riveting and revealing exploration of the world created by computer algorithms and its impact on individuals, from the workers across the globe who feed artificial intelligence systems with data to the impact of algorithms on our own behaviour, as consumers and citizens.

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Book SynopsisDonald E. Knuth has been making foundational contributions to the field of computer science for as long as computer science has been a field. His award-winning textbooks are often given credit for shaping the field, and his scientific papers are widely referenced and stand as milestones of development for a wide variety of topics. The present volume, the seventh in a series of his collected papers, is devoted to his work on the design of new algorithms. Nearly thirty of Knuth's classic papers are collected in this book and brought up to date with extensive revisions and notes on subsequent developments. The papers cover numerous discrete problems, such as assorting, searching, data compression, theorem proving, and cryptography, as well as methods for controlling errors in numerical computations.

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