Programming HTML5 Applications

Do you need to know how to write systems, services, and applications using the TinyOS operating system? Learn how to write nesC code and efficient applications with this indispensable guide to TinyOS programming. Detailed examples show you how to write TinyOS code in full, from basic applications right up to new low-level systems and high performance applications. Two leading figures in the development of TinyOS also explain the reasons behind many of the design decisions made and, for the first time, how nesC relates to and differs from other C dialects. Handy features such as a library of software design patterns, programming hints and tips, end-of-chapter exercises, and an appendix summarizing the basic application-level TinyOS APIs make this the ultimate guide to TinyOS for embedded systems programmers, developers, designers, and graduate students.

What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs? This valuable guide covers such elementary questions and more. It provides a state-of-the-art overview of the theoretical underpinnings of modern probabilistic programming and their applications in machine learning, security, and other domains, at a level suitable for graduate students and non-experts in the field. In addition, the book treats the connection between probabilistic programs and mathematical logic, security (what is the probability that software leaks confidential information?), and presents three programming languages for different applications: Excel tables, program testing, and approximate computing. This title is also available as Open Access on Cambridge Core.

Providing in-depth coverage, this book covers the fundamentals of computation and programming in C language. Essential concepts including operators and expressions, input and output statements, loop statements, arrays, pointers, functions, strings and preprocessors are described in a lucid manner. A unique approach - 'Learn by quiz' - features questions based on confidence-based learning methodology. It helps the reader to identify the right answer with adequate explanation and reasoning as to why the other options are incorrect. Computer programs and review questions are interspersed throughout the text. The book is appropriate for undergraduate students of engineering, computer science and information technology. It can be used for self-study and assists in the understanding of theoretical concepts and their applications.

This comprehensive introduction to the principles of functional programming using F# shows how to apply basic theoretical concepts to produce succinct and elegant programs. It demonstrates the role of functional programming in a wide spectrum of applications including databases and systems. Coverage also includes advanced features in the .NET library, the imperative features of F# and topics such as text processing, sequences, computation expressions and asynchronous computation. With a broad spectrum of examples and exercises, the book is perfect for courses in functional programming and for self-study. Enhancing its use as a text is an accompanying website with downloadable programs, lecture slides, mini-projects and links to further F# sources.

This comprehensive introduction to the principles of functional programming using F# shows how to apply basic theoretical concepts to produce succinct and elegant programs. It demonstrates the role of functional programming in a wide spectrum of applications including databases and systems. Coverage also includes advanced features in the .NET library, the imperative features of F# and topics such as text processing, sequences, computation expressions and asynchronous computation. With a broad spectrum of examples and exercises, the book is perfect for courses in functional programming and for self-study. Enhancing its use as a text is an accompanying website with downloadable programs, lecture slides, mini-projects and links to further F# sources.

Avoiding overly large blocks of code used in most other database programming books, this book shows a simple and easy way to create database programs and explains how to build professional and practical databases more efficiently. In addition to Design Tools and Wizards, the runtime object method is also discussed and analyzed to let users design and implement more sophisticated data-driven applications with complicated coding techniques. Three popular database systems – Microsoft Access, SQL Server 2005, and Oracle Database 10g Express Edition (XE) – are discussed in detail, with practical examples and sample projects. This book will appeal to college students, programmers, and software engineers alike. Sample code and additional exercise questions for students, as well as solutions and lecture slides for instructors, are available via the Web (www.cambridge.org/9780521712354).

Avoiding overly large blocks of code used in most other database programming books, this book shows a simple and easy way to create database programs and explains how to build professional and practical databases more efficiently. In addition to Design Tools and Wizards, the runtime object method is also discussed and analyzed to let users design and implement more sophisticated data-driven applications with complicated coding techniques. Three popular database systems – Microsoft Access, SQL Server 2005, and Oracle Database 10g Express Edition (XE) – are discussed in detail, with practical examples and sample projects. This book will appeal to college students, programmers, and software engineers alike. Sample code and additional exercise questions for students, as well as solutions and lecture slides for instructors, are available via the Web (www.cambridge.org/9780521712354).

Concurrent Programming in ML focuses on the practical use of concurrency to implement naturally concurrent applications. In addition to a tutorial introduction to programming in Concurrent ML (CML), the book presents three extended examples using CML for practical systems programming: a parallel software build system, a simple concurrent window manager, and an implementation of distributed tuple spaces. CML, which is included as part of the SML of New Jersey (SML/NJ) distribution, combines the best features of concurrent programming and functional programming. This book also illustrates advanced SML programming techniques, and includes a chapter on the implementation of concurrency using features provided by the SML/NJ system. It will be of interest to programmers, students, and professional researchers working in computer language development.

Declarative programs consist of mathematical functions and relations and are amenable to formal specification and verification, since the methods of logic and proof can be applied to the programs in a well-defined manner. Here Dr Padawitz emphasizes verification based on logical inference rules, i.e. deduction (in contrast with model-theoretic approaches, deductive methods can be automated to some extent). His treatment of the subject differs from others in that he tries to capture the actual styles and applications of programming; neither too general with respect to the underlying logic, nor too restrictive for the practice of programming. He generalizes and unifies results from classical theorem-proving and term rewriting to provide proof methods tailored to declarative program synthesis and verification. Detailed examples accompany the development of the methods, whose use is supported by a documented prototyping system. The book can be used for graduate courses or as a reference fo

Michael McMillan provides a complete presentation of the object-oriented features of the Visual Basic .NET language for advanced Visual Basic programmers. Beginning with an introduction to abstract data types and their initial implementation using structures, he explains standard OOP topics including class design, inheritance, access modifiers and scoping issues, abstract classes, design and implemention of interfaces and design patterns, and refactoring in VB.NET. More advanced OOP topics are included as well, such as reflection, object persistence, and serialization. To tie everything together, McMillan demonstrates sound OOP design and implementation principles through practical examples of standard Windows applications, database applications using ADO.NET, Web-based applications using ASP.NET, and Windows service applications.

Concurrent Programming in ML focuses on the practical use of concurrency to implement naturally concurrent applications. In addition to a tutorial introduction to programming in Concurrent ML (CML), the book presents three extended examples using CML for practical systems programming: a parallel software build system, a simple concurrent window manager, and an implementation of distributed tuple spaces. CML, which is included as part of the SML of New Jersey (SML/NJ) distribution, combines the best features of concurrent programming and functional programming. This book also illustrates advanced SML programming techniques, and includes a chapter on the implementation of concurrency using features provided by the SML/NJ system. It will be of interest to programmers, students, and professional researchers working in computer language development.

CUDA is now the dominant language used for programming GPUs, one of the most exciting hardware developments of recent decades. With CUDA, you can use a desktop PC for work that would have previously required a large cluster of PCs or access to a HPC facility. As a result, CUDA is increasingly important in scientific and technical computing across the whole STEM community, from medical physics and financial modelling to big data applications and beyond. This unique book on CUDA draws on the author's passion for and long experience of developing and using computers to acquire and analyse scientific data. The result is an innovative text featuring a much richer set of examples than found in any other comparable book on GPU computing. Much attention has been paid to the C++ coding style, which is compact, elegant and efficient. A code base of examples and supporting material is available online, which readers can build on for their own projects.

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box–Muller and Metropolis–Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data

This innovative book models pollution mitigation as a negative externality whilst also providing desirable and useful solutions, such as establishing the triangular equivalence relationship among the Lindahl equilibrium without transfers, the Nash bargaining solution with the payoffs of the Cournot-Nash equilibrium as the status quo point, and the social optimum under the Lindahl weights. By introducing programming algorithms to validate these relationships numerically, Zili Yang bridges the gap between analytical results and empirical modelling, ultimately solving the Lindahl equilibrium and hybrid Nash equilibria in the influential RICE model. This text demonstrates the complexity and variety of environment externality problems, ranging from mixed externality to correlated externalities to environmental externality under IRS and policy applications. Integrating theory, algorithms and applications in a comprehensive framework, The Environment and Externality will benefit scholars and

This innovative book models pollution mitigation as a negative externality whilst also providing desirable and useful solutions, such as establishing the triangular equivalence relationship among the Lindahl equilibrium without transfers, the Nash bargaining solution with the payoffs of the Cournot-Nash equilibrium as the status quo point, and the social optimum under the Lindahl weights. By introducing programming algorithms to validate these relationships numerically, Zili Yang bridges the gap between analytical results and empirical modelling, ultimately solving the Lindahl equilibrium and hybrid Nash equilibria in the influential RICE model. This text demonstrates the complexity and variety of environment externality problems, ranging from mixed externality to correlated externalities to environmental externality under IRS and policy applications. Integrating theory, algorithms and applications in a comprehensive framework, The Environment and Externality will benefit scholars and

Want to kill it at your job interview in the tech industry? Want to win that coding competition? Learn all the algorithmic techniques and programming skills you need from two experienced coaches, problem setters, and jurors for coding competitions. The authors highlight the versatility of each algorithm by considering a variety of problems and show how to implement algorithms in simple and efficient code. Readers can expect to master 128 algorithms in Python and discover the right way to tackle a problem and quickly implement a solution of low complexity. Classic problems like Dijkstra's shortest path algorithm and Knuth-Morris-Pratt's string matching algorithm are featured alongside lesser known data structures like Fenwick trees and Knuth's dancing links. The book provides a framework to tackle algorithmic problem solving, including: Definition, Complexity, Applications, Algorithm, Key Information, Implementation, Variants, In Practice, and Problems. Python code included in the book

This unique text provides a thorough, yet accessible, grounding in the mathematics, statistics, and programming that students need to master for coursework and research in climate science, meteorology, and oceanography. Assuming only high school mathematics, it presents carefully selected concepts and techniques in linear algebra, statistics, computing, calculus and differential equations within the context of real climate science examples. Computational techniques are integrated to demonstrate how to visualize, analyze, and apply climate data, with R code featured in the book and both R and Python code available online. Exercises are provided at the end of each chapter with selected solutions available to students to aid self-study and further solutions provided online for instructors only. Additional online supplements to aid classroom teaching include datasets, images, and animations. Guidance is provided on how the book can support a variety of courses at different levels, making i

Organizations and businesses strive toward excellence, and solutions to problems are based mostly on judgment and experience. However, increased competition and consumer demands require that the solutions be optimum and not just feasible. Theory leads to algorithms. Algorithms need to be translated into computer codes. Engineering problems need to be modeled. Optimum solutions are obtained using theory and computers, and then interpreted. Revised and expanded in its third edition, this textbook integrates theory, modeling, development of numerical methods, and problem solving, thus preparing students to apply optimization to real-world problems. This text covers a broad variety of optimization problems using: unconstrained, constrained, gradient, and non-gradient techniques; duality concepts; multi-objective optimization; linear, integer, geometric, and dynamic programming with applications; and finite element-based optimization. It is ideal for advanced undergraduate or graduate cours

Designed primarily for undergraduates, but also graduates and practitioners, this textbook integrates numerical methods and programming with applications from chemical engineering. Combining mathematical rigor with an informal writing style, it thoroughly introduces the theory underlying numerical methods, its translation into MATLAB programs, and its use for solving realistic problems. Specific topics covered include accuracy, convergence and numerical stability, as well as stiffness and ill-conditioning. MATLAB codes are developed from scratch, and their implementation is explained in detail, all while assuming limited programming knowledge. All scripts employed are downloadable, and built-in MATLAB functions are discussed and contextualised. Numerous examples and homework problems - from simple questions to extended case studies - accompany the text, allowing students to develop a deep appreciation for the range of real chemical engineering problems that can be solved using numerica

Software is pervasive in our lives. We are accustomed to dealing with the failures of much of that software - restarting an application is a very familiar solution. Such solutions are unacceptable when the software controls our cars, airplanes and medical devices or manages our private information. These applications must run without error. SPARK provides a means, based on mathematical proof, to guarantee that a program has no errors. SPARK is a formally defined programming language and a set of verification tools specifically designed to support the development of software used in high integrity applications. Using SPARK, developers can formally verify properties of their code such as information flow, freedom from runtime errors, functional correctness, security properties and safety properties. Written by two SPARK experts, this is the first introduction to the just-released 2014 version. It will help students and developers alike master the basic concepts for building systems with

Software is pervasive in our lives. We are accustomed to dealing with the failures of much of that software - restarting an application is a very familiar solution. Such solutions are unacceptable when the software controls our cars, airplanes and medical devices or manages our private information. These applications must run without error. SPARK provides a means, based on mathematical proof, to guarantee that a program has no errors. SPARK is a formally defined programming language and a set of verification tools specifically designed to support the development of software used in high integrity applications. Using SPARK, developers can formally verify properties of their code such as information flow, freedom from runtime errors, functional correctness, security properties and safety properties. Written by two SPARK experts, this is the first introduction to the just-released 2014 version. It will help students and developers alike master the basic concepts for building systems with

Cloud computing promises to revolutionize IT and business by making computing available as a utility over the internet. This book is intended primarily for practising software architects who need to assess the impact of such a transformation. It explains the evolution of the internet into a cloud computing platform, describes emerging development paradigms and technologies, and discusses how these will change the way enterprise applications should be architected for cloud deployment. Gautam Shroff provides a technical description of cloud computing technologies, covering cloud infrastructure and platform services, programming paradigms such as MapReduce, as well as 'do-it-yourself' hosted development tools. He also describes emerging technologies critical to cloud computing. The book also covers the fundamentals of enterprise computing, including a technical introduction to enterprise architecture, so it will interest programmers aspiring to become software architects and serve as a re

This book provides an integrated treatment of the theory of nonnegative matrices (matrices with only positive numbers or zero as entries) and some related classes of positive matrices, concentrating on connections with game theory, combinatorics, inequalities, optimisation and mathematical economics. The wide variety of applications, which include price fixing, scheduling and the fair division problem, have been carefully chosen both for their elegant mathematical content and for their accessibility to students with minimal preparation. Many results in matrix theory are also presented. The treatment is rigorous and almost all results are proved completely. These results and applications will be of great interest to researchers in linear programming, statistics and operations research. The minimal prerequisites also make the book accessible to first-year graduate students.

Ada is the only ISO-standard, object-oriented, concurrent, real-time programming language. It is intended for use in large, long-lived applications where reliability and efficiency are essential, particularly real-time and embedded systems. In this book, Alan Burns and Andy Wellings give a thorough, self-contained account of how the Ada tasking model can be used to construct a wide range of concurrent and real-time systems. This is the only book that focuses on an in-depth discussion of the Ada tasking model. Following on from the authors' earlier title Concurrency in Ada, this book brings the discussion up to date to include the new Ada 2005 language and the recent advances in real-time programming techniques. It will be of value to software professionals and advanced students of programming alike: indeed every Ada programmer will find it essential reading and a primary reference work that will sit alongside the language reference manual.

This treatment provides an exposition of discrete time dynamic processes evolving over an infinite horizon. Chapter 1 reviews some mathematical results from the theory of deterministic dynamical systems, with particular emphasis on applications to economics. The theory of irreducible Markov processes, especially Markov chains, is surveyed in Chapter 2. Equilibrium and long run stability of a dynamical system in which the law of motion is subject to random perturbations is the central theme of Chapters 3-5. A unified account of relatively recent results, exploiting splitting and contractions, that have found applications in many contexts is presented in detail. Chapter 6 explains how a random dynamical system may emerge from a class of dynamic programming problems. With examples and exercises, readers are guided from basic theory to the frontier of applied mathematical research.

This treatment provides an exposition of discrete time dynamic processes evolving over an infinite horizon. Chapter 1 reviews some mathematical results from the theory of deterministic dynamical systems, with particular emphasis on applications to economics. The theory of irreducible Markov processes, especially Markov chains, is surveyed in Chapter 2. Equilibrium and long run stability of a dynamical system in which the law of motion is subject to random perturbations is the central theme of Chapters 3-5. A unified account of relatively recent results, exploiting splitting and contractions, that have found applications in many contexts is presented in detail. Chapter 6 explains how a random dynamical system may emerge from a class of dynamic programming problems. With examples and exercises, readers are guided from basic theory to the frontier of applied mathematical research.

Optimal Reliability Design, first published in 2000, provides a detailed introduction to systems reliability and reliability optimization. Techniques for maximizing system reliability are described, focusing on component reliability enhancement and redundancy arrangement. The authors present several case studies and show how optimization techniques are applied in practice. They also pay particular attention to finding methods that give the optimal trade-off between reliability and cost. The book begins with a review of key background material, and a discussion of a range of optimization models. The authors go on to cover optimization tools, such as heuristics, discrete optimization, nonlinear programming, mixed integer programming, optimal arrangement, and metaheuristic algorithms. They also describe the computational implementation of these tools. Many numerical examples are included, and the book contains over 180 homework exercises. It is suitable as a textbook for graduate-level co

This book gives applications of the theory of process algebra, or Algebra of Communicating Processes (ACP), that is the study of concurrent or communicating processes studied using an algebraic framework. The approach is axiomatic; the authors consider structures that are some set of mostly equational axioms, which are equipped with several operators. Thus the term 'algebra' is used in the model-theoretic sense. The axiomatic approach enables one to organize the field of process theories. The theory is applied systematically to a number of situations, including systolic algorithms, semantics of an object-oriented language, and protocols. It will be welcomed by computer scientists working in parallel programming.

Learn how to write technical applications in a modern object-oriented approach, using Fortran 90 or 95. This book will teach you how to stop focusing on the traditional procedural abilities of Fortran and to employ the principles of object-oriented programming to produce clear, highly efficient executable codes. Get ready now to take advantage of all the features of the finalized, fully object-oriented Fortran 200X! In addition to covering the OOP methodologies the book also covers the basic foundation of the language and good programming skills, making the book valuable also as a good migration tool for experienced Fortran programmers who want to smoothly pick up the OOP paradigm. The author highlights common themes by using comparisons with Matlab and C++ and uses numerous cross-referenced examples to convey all concepts quickly and clearly. Complete code for the examples is included on the book's web page.

Optimal Reliability Design, first published in 2000, provides a detailed introduction to systems reliability and reliability optimization. Techniques for maximizing system reliability are described, focusing on component reliability enhancement and redundancy arrangement. The authors present several case studies and show how optimization techniques are applied in practice. They also pay particular attention to finding methods that give the optimal trade-off between reliability and cost. The book begins with a review of key background material, and a discussion of a range of optimization models. The authors go on to cover optimization tools, such as heuristics, discrete optimization, nonlinear programming, mixed integer programming, optimal arrangement, and metaheuristic algorithms. They also describe the computational implementation of these tools. Many numerical examples are included, and the book contains over 180 homework exercises. It is suitable as a textbook for graduate-level co

Functional programming is a style of programming that emphasizes the use of functions (in contrast to object-oriented programming, which emphasizes the use of objects). It has become popular in recent years because of its simplicity, conciseness, and clarity. This book teaches functional programming as a way of thinking and problem solving, using Haskell, the most popular purely functional language. Rather than using the conventional (boring) mathematical examples commonly found in other programming language textbooks, the author uses examples drawn from multimedia applications, including graphics, animation, and computer music, thus rewarding the reader with working programs for inherently more interesting applications. Aimed at both beginning and advanced programmers, this tutorial begins with a gentle introduction to functional programming and moves rapidly on to more advanced topics. Details about progamming in Haskell are presented in boxes throughout the text so they can be easil

Functional programming is a style of programming that emphasizes the use of functions (in contrast to object-oriented programming, which emphasizes the use of objects). It has become popular in recent years because of its simplicity, conciseness, and clarity. This book teaches functional programming as a way of thinking and problem solving, using Haskell, the most popular purely functional language. Rather than using the conventional (boring) mathematical examples commonly found in other programming language textbooks, the author uses examples drawn from multimedia applications, including graphics, animation, and computer music, thus rewarding the reader with working programs for inherently more interesting applications. Aimed at both beginning and advanced programmers, this tutorial begins with a gentle introduction to functional programming and moves rapidly on to more advanced topics. Details about progamming in Haskell are presented in boxes throughout the text so they can be easil

This book provides an integrated treatment of the theory of nonnegative matrices (matrices with only positive numbers or zero as entries) and some related classes of positive matrices, concentrating on connections with game theory, combinatorics, inequalities, optimisation and mathematical economics. The wide variety of applications, which include price fixing, scheduling and the fair division problem, have been carefully chosen both for their elegant mathematical content and for their accessibility to students with minimal preparation. Many results in matrix theory are also presented. The treatment is rigorous and almost all results are proved completely. These results and applications will be of great interest to researchers in linear programming, statistics and operations research. The minimal prerequisites also make the book accessible to first-year graduate students.

Here is an introduction to functional programming and its associated systems. A unique feature is its use of the language Haskell for teaching both the rudiments and the finer points of the functional technique. Haskell is a new, internationally agreed and accepted functional language that is designed for teaching, research and applications, that has a complete formal description, that is freely available, and that is based on ideas that have a wide consensus. Thus it encapsulates some of the main thrusts of functional programming itself, which is a style of programming designed to confront the software crisis directly. Programs written in functional languages can be built up from smaller parts, and they can also be proved correct, important when software has to be reliable. Moreover, a certain amount of parallelism can be extracted from functional languages automatically. This book serves as an introduction both to functional programming and Haskell, and will be most useful to student

Discover the subject of optimization in a new light with this modern and unique treatment. Includes a thorough exposition of applications and algorithms in sufficient detail for practical use, while providing you with all the necessary background in a self-contained manner. Features a deeper consideration of optimal control, global optimization, optimization under uncertainty, multiobjective optimization, mixed-integer programming and model predictive control. Presents a complete coverage of formulations and instances in modelling where optimization can be applied for quantitative decision-making. As a thorough grounding to the subject, covering everything from basic to advanced concepts and addressing real-life problems faced by modern industry, this is a perfect tool for advanced undergraduate and graduate courses in chemical and biochemical engineering.

The arrival and popularity of multi-core processors has sparked a renewed interest in the development of parallel programs. Similarly, the availability of low-cost microprocessors and sensors has generated a great interest in embedded real-time programs. This book provides students and programmers whose backgrounds are in traditional sequential programming with the opportunity to expand their capabilities into parallel, embedded, real-time and distributed computing. It also addresses the theoretical foundation of real-time scheduling analysis, focusing on theory that is useful for actual applications. Written by award-winning educators at a level suitable for undergraduates and beginning graduate students, this book is the first truly entry-level textbook in the subject. Complete examples allow readers to understand the context in which a new concept is used, and enable them to build and run the examples, make changes, and observe the results.

This book develops the theory of typed feature structures, a data structure that generalizes both first-order terms and feature structures of unification-based grammars to include inheritance, typing, inequality, cycles and intensionality. The resulting synthesis serves as a logical foundation for grammars, logic programming and constraint-based reasoning systems. A logical perspective is adopted which employs an attribute-value description language along with complete equational axiomatizations of the various systems of feature structures. At the same time, efficiency concerns are kept in mind and complexity and representability results are provided. The application of feature structures to phrase structure grammars is described and completeness results are shown for standard evaluation strategies. Definite clause logic programs are treated as a special case of phrase structure grammars. Constraint systems are introduced and an enumeration technique is developed for solving arbitrary

Deep brain stimulation (DBS) is now a firmly established treatment for movement disorders, and an increasing body of evidence supports DBS in the treatment of other neurological and psychiatric disorders. This essential reference guide outlines a practical approach to the use of this paradigm-shifting therapy and covers key aspects of DBS practice. Chapters describe how to implement a DBS program and select appropriate patients, device programming to achieve optimal symptom control, and long-term management of patients. Thoroughly revised, this third edition includes additional chapters on managing patients with emerging applications of DBS. An entire chapter is dedicated to troubleshooting common problems with the therapy as many 'failures' are preventable and addressable. With contributions from experts in the field, this is a must-have reference guide for any clinician working with DBS patients.

This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendice

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.