Machine Learning Algorithms for Problem Solving in Computational Applications

The first comprehensive book on thin-film solar cells, potentially a key technology for solving the energy production problem in the 21st century in an environmentally friendly way. It covers a wide r

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

A unique, applied approach to problem solving in linear algebraDeparting from the standard methods of analysis, this unique book presents methodologies and algorithms based on the concept of orthogona

This intermediate algebra text was developed in the spirit of the AMATYC standards for problem-solving, modeling, connecting with other disciplines, technology, and calculus reform. Many traditional t

Foster early learning and problem-solving skills with this beautifully crafted wooden peg puzzle featuring Tabitha Paige's signature watercolor farm animals. Tabitha Paige's beloved watercolor animals come to life in a timeless, tactile way with this premium wooden peg puzzle for toddlers and preschoolers. Designed for little hands, each of the 8 large, easy-to-grasp animal shapes fits neatly into its corresponding slot, encouraging shape recognition, animal vocabulary, and hand-eye coordination. Plus, the back of the puzzle board features a bonus farm scene for interactive play and storytelling. Crafted from sturdy, child-safe wood and printed with Tabitha's warm, lifelike artwork, this puzzle blends play with learning, making it a beautiful addition to any nursery or playroom. Parents will love its educational value, and children will delight in the charming farm friends waiting to be discovered.

Federated Learning for the Metaverse: Applications in Virtual Environments provides readers with insights into how federated learning, a decentralized machine learning paradigm, can be strategically applied to address critical aspects of the metaverse. The book covers a wide range of topics, including privacy-preserving personalization, security, collaboration, adaptive learning environments, real-time communication, decentralized governance, language understanding, immersive learning experiences, avatar customization, and dynamic scene rendering.

Longlisted for the 2024 Financial Times Book of the Year. How life and the economy became a black box--a collection of systems no one understands, producing outcomes no one likes. Passengers get bumped from flights. Phone menus disconnect. Automated financial trades produce market collapse. Of all the challenges in modern life, some of the most vexing come from our relationships with automation: a large system does us wrong, and there's nothing we can do about it. The problem, economist Dan Davies shows, is accountability sinks: systems in which decisions are delegated to a complex rule book or set of standard procedures, making it impossible to identify the source of mistakes when they happen. In our increasingly unhuman world--lives dominated by algorithms, artificial intelligence, and large organizations--these accountability sinks produce more than just aggravation. They make life and economy unknowable--a black box for no reason. In The Unaccountability Machine, Davies lays bar

Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This

No magical creature has a problem too big for Ava and her Dads to solve!Unicorns with tummy trouble. Dragons with broken wings. Sneezy centaurs and mermaids with measles.Everyone in town knows just who to see when they are feeling poorly: Ava and her dads, the Marvellous Doctors for Magical Creatures. Well, Ava isn't a doctor, not yet, but she's learning the ropes and she's brilliant at solving medical mysteries. Glitterbug the unicorn is not like all the other unicorns.They love dancing and planning parties, but she would much rather be frolicking in the mud. Glitterbug comes to the Marvellous Doctors with a terrible tummy ache, but after examining her, they are mystified as to what's causing it. Can Ava help her dads find the answer?In this magical story about being true to yourself, Ava discovers there's more than one way to be a unicorn!

Introduce your little one to the wonders of nature with 100+ outdoor learning activities!Give your child all the benefits of nature with this activity book designed for children ages 2-5. Whether you’re planning an afternoon in a local park, a day in the backyard, or a morning hike in a nature preserve, you and your child will find more than 100 ideas in these pages for observing, exploring, creating, and playing in nature.Nature Activity Book for Little Ones features: 100+ outdoor activities for kids ages 2-5, geared toward their interests and developmental milestones—with little or no preparation requiredBonus activities for additional learning, including numbers, letters, shapes, and colorsSchool prep: the activities teach problem-solving, communication, and responsibility, important skills for school succesLess screen time and more nature time together to fuel your child’s curiosity and creativity and help them build an early connection with natureBest of all, this entertaining boo

The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer performance to harness it for future applications. Ideal for upper level undergraduates, Computer Architecture for Scientists covers four key pillars of computer performance and imparts a high-level basis for reasoning with and understanding these concepts: Small is fast – how size scaling drives performance; Implicit parallelism – how a sequential program can be executed faster with parallelism; Dynamic locality – skirting physical limits, by arranging data in a smaller space; Parallelism – increasing performance with teams of workers. These principles and models provide approachable high-level insights and quantitative modelling without distracting low-level detail. Finally, the text covers the GPU and machine-learning accelerat

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found

Now in its third edition, this textbook develops the economic way of thinking through problems that MBA students will find relevant to their career goals. Theory and mathematics are kept as simple as possible and illustrated with real-life scenarios. The textbook package includes online video tutorials on key concepts and complex arguments, and topics likely to be assessed in exams. The distinguished author team has developed this textbook over twenty years of teaching microeconomics to MBA students. Chapters are clearly structured to support learning: Part I of each chapter develops key economic principles, whilst Part II draws on those principles to discuss organizational and incentive issues in management and focuses on solving the 'principal-agent' problem to maximize the profitability of the firm - lessons that can be applied to problems MBA students will face in the future. Economics and management are treated equally; this unique textbook presents economics as part of the everyd

Anchored in simple and familiar physics problems, the author provides a focused introduction to mathematical methods in a narrative driven and structured manner. Ordinary and partial differential equation solving, linear algebra, vector calculus, complex variables and numerical methods are all introduced and bear relevance to a wide range of physical problems. Expanded and novel applications of these methods highlight their utility in less familiar areas, and advertise those areas that will become more important as students continue. This highlights both the utility of each method in progressing with problems of increasing complexity while also allowing students to see how a simplified problem becomes 're-complexified'. Advanced topics include nonlinear partial differential equations, and relativistic and quantum mechanical variants of problems like the harmonic oscillator. Physics, mathematics and engineering students will find 300 problems treated in a sophisticated manner. The insig

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overv

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overv

"This book disseminates practical solutions to the automation of open and distance learning. It discusses computational methods, algorithms, implemented prototype systems, and applications of open and

This valuable resource summarizes the past fifty years' basic research accomplishments in plasma dynamics for aerospace engineering, presenting these results in a comprehensive volume that will be an asset to any professional in the field. It offers a comprehensive review of the foundation of plasma dynamics while integrating the most recently developed modeling and simulation techniques with the theoretic physics, including the state-of-the-art numerical algorithms. Several first-ever demonstrations for innovations and incisive explanations for previously unexplained observations are included. All the necessary formulations for technical evaluation to engineering applications are derived from the first principle by statistic and quantum mechanics, and led to physics-based computational simulations for practical applications. The computer-aided procedures directly engage the reader to duplicate findings that are nearly impossible by using ground-based experimental facilities. Plasma Dy

Over the past several decades, computational approaches to studying strongly-interacting systems have become increasingly varied and sophisticated. This book provides a comprehensive introduction to state-of-the-art quantum Monte Carlo techniques relevant for applications in correlated systems. Providing a clear overview of variational wave functions, and featuring a detailed presentation of stochastic samplings including Markov chains and Langevin dynamics, which are developed into a discussion of Monte Carlo methods. The variational technique is described, from foundations to a detailed description of its algorithms. Further topics discussed include optimisation techniques, real-time dynamics and projection methods, including Green's function, reptation and auxiliary-field Monte Carlo, from basic definitions to advanced algorithms for efficient codes, and the book concludes with recent developments on the continuum space. Quantum Monte Carlo Approaches for Correlated Systems provides

Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive

From designing a residential street and your perfect play park to learning some of the math skills used by architects, how to read floor plans, and the basics of interior design, the activities in this innovative book encourage imaginative play and problem solving. Includes stickers, a pull-out poster, and press-out game cards.

This book presents a significant advancement in the theory and practice of knowledge engineering, the discipline concerned with the development of intelligent agents that use knowledge and reasoning to perform problem solving and decision-making tasks. It covers the main stages in the development of a knowledge-based agent: understanding the application domain, modeling problem solving in that domain, developing the ontology, learning the reasoning rules, and testing the agent. The book focuses on a special class of agents: cognitive assistants for evidence-based reasoning that learn complex problem-solving expertise directly from human experts, support experts, and nonexperts in problem solving and decision making, and teach their problem-solving expertise to students. A powerful learning agent shell, Disciple-EBR, is included with the book, enabling students, practitioners, and researchers to develop cognitive assistants rapidly in a wide variety of domains that require evidence-base

Now in its third edition, this textbook develops the economic way of thinking through problems that MBA students will find relevant to their career goals. Theory and mathematics are kept as simple as possible and illustrated with real-life scenarios. The textbook package includes online video tutorials on key concepts and complex arguments, and topics likely to be assessed in exams. The distinguished author team has developed this textbook over twenty years of teaching microeconomics to MBA students. Chapters are clearly structured to support learning: Part I of each chapter develops key economic principles, whilst Part II draws on those principles to discuss organizational and incentive issues in management and focuses on solving the 'principal-agent' problem to maximize the profitability of the firm - lessons that can be applied to problems MBA students will face in the future. Economics and management are treated equally; this unique textbook presents economics as part of the everyd

Optimization is used to determine the most appropriate value of variables under given conditions. The primary focus of using optimisation techniques is to measure the maximum or minimum value of a function depending on the circumstances. This book discusses problem formulation and problem solving with the help of algorithms such as secant method, quasi-Newton method, linear programming and dynamic programming. It also explains important chemical processes such as fluid flow systems, heat exchangers, chemical reactors and distillation systems using solved examples. The book begins by explaining the fundamental concepts followed by an elucidation of various modern techniques including trust-region methods, Levenberg–Marquardt algorithms, stochastic optimization, simulated annealing and statistical optimization. It studies the multi-objective optimization technique and its applications in chemical engineering and also discusses the theory and applications of various optimization software

Your SECRET to creating GREAT Apple Software Today! Read this book for FREE on Kindle Unlimited NOW Do you want to create the next greatest iPhone app? Would you like a high-paying career in Progr

Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples

This book provides an argumentation model for means end-reasoning, a distinctive type of reasoning used for problem-solving and decision-making. Means end-reasoning is modelled as goal-directed argumentation from an agent's goals and known circumstances, and from an action selected as a means, to a decision to carry out the action. Goal-based Reasoning for Argumentation provides an argumentation model of this kind of reasoning showing how it is employed in settings of intelligent deliberation where agents try to collectively arrive at a conclusion on what they should do to move forward in a set of circumstances. The book explains how this argumentation model can help build more realistic computational systems of deliberation and decision-making, and shows how such systems can be applied to solve problems posed by goal-based reasoning in numerous fields, from social psychology and sociology, to law, political science, anthropology, cognitive science, artificial intelligence, multi-agent

This book provides an argumentation model for means end-reasoning, a distinctive type of reasoning used for problem-solving and decision-making. Means end-reasoning is modelled as goal-directed argumentation from an agent's goals and known circumstances, and from an action selected as a means, to a decision to carry out the action. Goal-based Reasoning for Argumentation provides an argumentation model of this kind of reasoning showing how it is employed in settings of intelligent deliberation where agents try to collectively arrive at a conclusion on what they should do to move forward in a set of circumstances. The book explains how this argumentation model can help build more realistic computational systems of deliberation and decision-making, and shows how such systems can be applied to solve problems posed by goal-based reasoning in numerous fields, from social psychology and sociology, to law, political science, anthropology, cognitive science, artificial intelligence, multi-agent

The recent appearance of wavelets as a new computational tool in applied mathematics has given a new impetus to the field of numerical analysis of Fredholm integral equations. This book gives an account of the state of the art in the study of fast multiscale methods for solving these equations based on wavelets. The authors begin by introducing essential concepts and describing conventional numerical methods. They then develop fast algorithms and apply these to solving linear, nonlinear Fredholm integral equations of the second kind, ill-posed integral equations of the first kind and eigen-problems of compact integral operators. Theorems of functional analysis used throughout the book are summarised in the appendix. The book is an essential reference for practitioners wishing to use the new techniques. It may also be used as a text, with the first five chapters forming the basis of a one-semester course for advanced undergraduates or beginning graduates.

Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how t

With an emphasis on applications of computational models for solving modern challenging problems in biomedical and life sciences, this book aims to bring collections of articles from biologists, medic

Computing is revolutionizing the practice of biology. This book, which assumes no prior computing experience, provides students with the tools to write their own Python programs and to understand fundamental concepts in computational biology and bioinformatics. Each major part of the book begins with a compelling biological question, followed by the algorithmic ideas and programming tools necessary to explore it: the origins of pathogenicity are examined using gene finding, the evolutionary history of sex determination systems is studied using sequence alignment, and the origin of modern humans is addressed using phylogenetic methods. In addition to providing general programming skills, this book explores the design of efficient algorithms, simulation, NP-hardness, and the maximum likelihood method, among other key concepts and methods. Easy-to-read and designed to equip students with the skills to write programs for solving a range of biological problems, the book is accompanied by nu

Computing is revolutionizing the practice of biology. This book, which assumes no prior computing experience, provides students with the tools to write their own Python programs and to understand fundamental concepts in computational biology and bioinformatics. Each major part of the book begins with a compelling biological question, followed by the algorithmic ideas and programming tools necessary to explore it: the origins of pathogenicity are examined using gene finding, the evolutionary history of sex determination systems is studied using sequence alignment, and the origin of modern humans is addressed using phylogenetic methods. In addition to providing general programming skills, this book explores the design of efficient algorithms, simulation, NP-hardness, and the maximum likelihood method, among other key concepts and methods. Easy-to-read and designed to equip students with the skills to write programs for solving a range of biological problems, the book is accompanied by nu

Lev Vygotsky's approach to children's learning and development centers on adult mediation: adults engage children in age-appropriate activities and promote in this context the development of new motives and tools for thinking, problem solving, and self-regulation. Although this approach has earned widespread recognition in the global scientific community, English-speaking educators remain relatively unfamiliar with its contemporary elaborations for practical application. Yuriy V. Karpov offers the first comprehensive English introduction to contemporary elaborations of Vygotsky's ideas and their practical applications from birth through adolescence. He demonstrates the advantages of the Vygotskian approach over both traditional and constructivist education. This volume will prove an invaluable resource for educators and students in teacher education programs, as well as for everyone interested in educational and developmental psychology.