Machine Learning Algorithms for Problem Solving in Computational Applications

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

What happens to risk as the economic horizon goes to zero and risk is seen as an exposure to a change in state that may occur instantaneously at any time? All activities that have been undertaken statically at a fixed finite horizon can now be reconsidered dynamically at a zero time horizon, with arrival rates at the core of the modeling. This book, aimed at practitioners and researchers in financial risk, delivers the theoretical framework and various applications of the newly established dynamic conic finance theory. The result is a nonlinear non-Gaussian valuation framework for risk management in finance. Risk-free assets disappear and low risk portfolios must pay for their risk reduction with negative expected returns. Hedges may be constructed to enhance value by exploiting risk interactions. Dynamic trading mechanisms are synthesized by machine learning algorithms. Optimal exposures are designed for option positioning simultaneously across all strikes and maturities.

Give your preschooler a strong computer science foundation with Smart Start: Beginning Coding Stories and Activities. Children will learn important coding concepts with engaging stories and practice activities. Based on the K-12 Computer Science Framework, this dynamic early learning series helps children understand coding concepts to develop a strong foundation in computer science, including computational thinking, problem solving, and critical thinking. Each full-color activity book is organized into eight themed units around one coding concept and includes: - Coding stories that demonstrate what coders do and include an audio read-aloud feature - Concept pages that connect coding concepts to real-world situations - Practice activities that reinforce important coding rules - Review pages for every coding concept practiced - Coding projects with print-based games and puzzles - Mini coding handbook with key terms Computer science concepts and skills help early learners become critical

Give your kindergartner a strong computer science foundation with Smart Start: Beginning Coding Stories and Activities. Children will learn important coding concepts with engaging stories and practice activities. Based on the K-12 Computer Science Framework, this dynamic early learning series helps children understand coding concepts to develop a strong foundation in computer science, including computational thinking, problem solving, and critical thinking. Each full-color activity book is organized into eight themed units around one coding concept and includes: - Coding stories that demonstrate what coders do and include an audio read-aloud feature - Concept pages that connect coding concepts to real-world situations - Practice activities that reinforce important coding rules - Review pages for every coding concept practiced - Coding projects with print-based games and puzzles - Mini coding handbook with key terms Computer science concepts and skills help early learners become critica

An accessible analysis of the new forms of work whose seismic changes will increasingly determine the future of capitalism Automation and the decline in industrial employment have lead to rising fears of a workless future. But what happens when your work itself is the thing that will make your job obsolete? In the past few years, online crowdworking platforms - like Amazon's Mechanical Turk and Clickworker - have become an increasingly important source of work, particularly for those in the Global South. Here, small tasks are assigned to people online, and are often used to train algorithms to spot patterns, patterns through machine learning those same algorithms will then be able to spot more effectively than humans. Used for everything from the mechanics of self-driving cars to Google image search, this is an increasingly powerful part of the digital ecomomy. But what happens to work when it makes itself obsolete. In this stimulating work that blends political economy, studies of con

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques to the detection, mitigation, treatment, and elimination of the COVID-19 virus around the world. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes. Data Science for COVID-19, Volume 2 extends the coverage of Volume 1 into research on statistical data and applications of Data Science to a variety of perspectives for analysis and understanding of the wide variety of impacts COVID-19 is having on societies and medical practice around the world.

This book integrates the foundations of quantum computing with a hands-on coding approach to this emerging field; it is the first to bring these strands together in an updated manner. This work is suitable for both academic coursework and corporate technical training.The second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correction, Dirac notation and more. This new edition benefits from the input of the many faculty, students, corporate engineering teams, and independent readers who have used the first edition.This volume comprises three books under one cover: Part I outlines the necessary foundations of quantum computing and quantum circuits. Part II walks through the canon of quantum computing algorithms and provides code on a range of quantum computing methods in current use. Part III covers the mathematical toolkit required to master quantum computing. Additional reso

This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of placing current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term "A.I." is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm.This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights

If education is to prepare learners for lifelong learning, there needs to be a shift towards deeper learning: a focus on transferable knowledge and problem-solving skills alongside the development of a positive or growth mindset. In this book, a follow up to CLIL, the authors review new developments in the understanding of the interface between language and learning, and propose an original new 'pluriliteracies' approach which refines and develops current thinking in CLIL. It aims to facilitate deeper learning through an explicit focus on disciplinary literacies, guiding learners towards textual fluency, encouraging successful communication across cultures, and providing a key stepping-stone towards becoming responsible global citizens. It both provides strong theoretical grounding, and shows how to put that understanding into practise. Engaging and practical, this book will be invaluable to both academics and education practitioners, and will enable conventional classrooms to be trans

The wave equation, a classical partial differential equation, has been studied and applied since the eighteenth century. Solving it in the presence of an obstacle, the scatterer, can be achieved using a variety of techniques and has a multitude of applications. This book explains clearly the fundamental ideas of time-domain scattering, including in-depth discussions of separation of variables and integral equations. The author covers both theoretical and computational aspects, and describes applications coming from acoustics (sound waves), elastodynamics (waves in solids), electromagnetics (Maxwell's equations) and hydrodynamics (water waves). The detailed bibliography of papers and books from the last 100 years cement the position of this work as an essential reference on the topic for applied mathematicians, physicists and engineers.

This innovative approach to teaching the finite element method blends theoretical, textbook-based learning with practical application using online and video resources. This hybrid teaching package features computational software such as MATLAB®, and tutorials presenting software applications such as PTC Creo Parametric, ANSYS APDL, ANSYS Workbench and SolidWorks, complete with detailed annotations and instructions so students can confidently develop hands-on experience. Suitable for senior undergraduate and graduate level classes, students will transition seamlessly between mathematical models and practical commercial software problems, empowering them to advance from basic differential equations to industry-standard modelling and analysis. Complete with over 120 end-of chapter problems and over 200 illustrations, this accessible reference will equip students with the tools they need to succeed in the workplace.

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 handbook lays out the science behind how animals think, remember, create, calculate, and remember. It provides concise overviews on major areas of study such as animal communication and language, memory and recall, social cognition, social learning and teaching, numerical and quantitative abilities, as well as innovation and problem solving. The chapters also explore more nuanced topics in greater detail, showing how the research was conducted and how it can be used for further study. The authors range from academics working in renowned university departments to those from research institutions and practitioners in zoos. The volume encompasses a wide variety of species, ensuring the breadth of the field is explored.

This handbook lays out the science behind how animals think, remember, create, calculate, and remember. It provides concise overviews on major areas of study such as animal communication and language, memory and recall, social cognition, social learning and teaching, numerical and quantitative abilities, as well as innovation and problem solving. The chapters also explore more nuanced topics in greater detail, showing how the research was conducted and how it can be used for further study. The authors range from academics working in renowned university departments to those from research institutions and practitioners in zoos. The volume encompasses a wide variety of species, ensuring the breadth of the field is explored.

Dealing with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed matter physics and statistical mechanics, this book provides an introduction to computer simulations in physics. The 5th edition contains extensive new material describing numerous powerful algorithms and methods that represent recent developments in the field. New topics such as active matter and machine learning are also introduced. Throughout, there are many applications, examples, recipes, case studies, and exercises to help the reader fully comprehend the material. This book is ideal for graduate students and researchers, both in academia and industry, who want to learn techniques that have become a third tool of physical science, complementing experiment and analytical theory.

Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed.

Imagine a world where machines can see and understand the world the way humans do. Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars that detect pedestrians, and algorithms that suggest diagnoses from clinical images, among many other applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing. This book introduces the neuroscientific study of neuronal computations in visual cortex alongside of the psychological understanding of visual cognition and the burgeoning field of biologically-inspired artificial intelligence. Topics include the neurophysiological investigation of visual cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among others. It is an ideal resource for students and researchers looking to build bridges across different approaches to studying and developing

Imagine a world where machines can see and understand the world the way humans do. Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars that detect pedestrians, and algorithms that suggest diagnoses from clinical images, among many other applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing. This book introduces the neuroscientific study of neuronal computations in visual cortex alongside of the psychological understanding of visual cognition and the burgeoning field of biologically-inspired artificial intelligence. Topics include the neurophysiological investigation of visual cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among others. It is an ideal resource for students and researchers looking to build bridges across different approaches to studying and developing

Artificial intelligence (AI) stands out as a transformational technology of the digital age. Its practical applications are growing very rapidly. One of the chief reasons AI applications are attaining prominence, is in its design to learn continuously, from real-world use and experience, and its capability to improve its performance.It is no wonder that the applications of AI span from complex high-technology equipment manufacturing to personalized exclusive recommendations to end-users. Many deployments of AI software, given its continuous learning need, require computation platforms that are resource intense, and have sustained connectivity and perpetual power through central electrical grid. In order to harvest the benefits of AI revolution to all of humanity, traditional AI software development paradigms must be upgraded to function effectively in environments that have resource constraints, small form factor computational devices with limited power, devices with intermittent or no

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.

The real world is perceived and broken down as data, models and algorithms in the eyes of physicists and engineers. Data is noisy by nature and classical statistical tools have so far been successful in dealing with relatively smaller levels of randomness. The recent emergence of Big Data and the required computing power to analyse them have rendered classical tools outdated and insufficient. Tools such as random matrix theory and the study of large sample covariance matrices can efficiently process these big data sets and help make sense of modern, deep learning algorithms. Presenting an introductory calculus course for random matrices, the book focusses on modern concepts in matrix theory, generalising the standard concept of probabilistic independence to non-commuting random variables. Concretely worked out examples and applications to financial engineering and portfolio construction make this unique book an essential tool for physicists, engineers, data analysts, and economists.

Mood disorders such as depression and bipolar disorder are common mental illnesses, affecting millions of patients worldwide. The application of newly available brain imaging methods to the study of mood disorders holds substantial promise in uncovering the brain mechanisms affected in these illnesses. This comprehensive and authoritative text features contributions from leading international experts, providing easily accessible information on the study of the brain mechanisms involved in the causation of mood disorders and the available treatments. Topics covered include the potential of magnetoencephalography (MEG), neuroimaging brain inflammation in depression, electrophysiology studies in mood disorders, and the applications of machine learning, filling an important gap in available neuropsychiatric literature and highlighting new developments. An invaluable resource for practitioners in the fields of psychiatry, neurology, primary care medicine, and related mental health professio

This textbook introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. To ensure student understanding, the text focuses on a relatively small number of core concepts with an abundance of illustrations and examples. Concepts are reinforced with hands-on exercises to nurture the student's skill in problem solving. New concepts and algorithms are framed by real-world context and established as part of the big picture introduced in an early chapter. A problem-solving strategy is employed in several chapters to equip students with an approach for new problems in pattern recognition. This text also points out common errors that a new player in pattern recognition may encounter, and fosters the ability for readers to find useful resources and independently solve a new pattern recognition task through various working examples. Students with an undergraduate understanding of mathematical analysis, linear algebra, an

Tough Test Questions? Missed Lectures? Not Enough Time?Fortunately, there’s Schaum’s. More than 40 million students have trusted Schaum’s to help them succeed in the classroom and on exams. Schaum’s is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format.You also get hundreds of examples, solved problems, and practice exercises to test your skills. This Schaum’s Outline gives you:628 fully solved problems to reinforce knowledge1 final practice examHundreds of examples with explanations of statics conceptsExtra practice on topics such as orthogonal triad of unit vectors, resultant of distributed force system, noncoplanar force systems, slope of the Shear diagram, and slope of the Moment diagramSupport for all the major textbooks for statics coursesAccess to revised Schaums.com website with access to 25 problem-solving videos and more. Schaum’s reinforces the main concepts req

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and als

Focus on the fundamentals and help students see connections between problem types Richard Wolfson's Essential University Physics is a concise and progressive calculus-based physics textbook that offers clear writing, great problems, and relevant real-life applications in an affordable and streamlined text. The book teaches sound problem-solving strategies and emphasises conceptual understanding, using features such as annotated figures and step-by-step problem-solving strategies. Realising students have changed a great deal over time while the fundamentals of physics have changed very little, Wolfson makes physics relevant and alive for students by sharing the latest physics applications in a succinct and captivating style.The 4th Edition, Global Edition, incorporates research from instructors, reviewers, and thousands of students to expand the book's problem sets and consistent problem-solving strategy. A new problem type guides students to see patterns, make connections between probl

Microeconomics is a classroom-tested resource for learning the key concepts, essential tools, and applications of microeconomics. This leading textbook enables students to recognize and analyze significant data, patterns, and trends in real markets through its integrated, student-friendly approach to the subject — providing practice problems, hands-on exercises, illustrative examples, and engaging applications that ground theory firmly in the real world. Each chapter, opening with a set of clearly defined learning goals based on the Bloom Taxonomy, features numerous Learning-by-Doing (LBD) problems, mathematical and graphical data, and varied problem sets focused on current events.Now in its sixth edition, the text offers extensive new and revised content throughout. All applications reflect current data and important new developments in the field of economics, including behavioral economics, randomized controlled trials (RCTs) in policy evaluation and design, and computational-based m

This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinic

This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinic

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix no

This bestselling textbook provides an engaging and user-friendly introduction to the study of language. Assuming no prior knowledge of the subject, Yule presents information in bite-sized sections, clearly explaining the major concepts in linguistics and all the key elements of language. This seventh edition has been revised and updated throughout, with substantial changes to the chapters on phonetics and semantics, and forty new study questions. To increase student engagement and to foster problem-solving and critical thinking skills, the book includes over twenty new tasks. An expanded and revised online study guide provides students with further resources, including answers and tutorials for all tasks, while encouraging lively and proactive learning. This is the most fundamental and easy-to-use introduction to the study of language.

Finite-state methods are the most efficient mechanisms for analysing textual and symbolic data, providing elegant solutions for an immense number of practical problems in computational linguistics and computer science. This book for graduate students and researchers gives a complete coverage of the field, starting from a conceptual introduction and building to advanced topics and applications. The central finite-state technologies are introduced with mathematical rigour, ranging from simple finite-state automata to transducers and bimachines as 'input-output' devices. Special attention is given to the rich possibilities of simplifying, transforming and combining finite-state devices. All algorithms presented are accompanied by full correctness proofs and executable source code in a new programming language, C(M), which focuses on transparency of steps and simplicity of code. Thus, by enabling readers to obtain a deep formal understanding of the subject and to put finite-state methods t

The tenth edition of Operating System Concepts has been revised to keep it fresh and up-to-date with contemporary examples of how operating systems function, and to improve learning and the student's experience with the material. It combines instruction on concepts with real-world applications so that students can understand the practical usage of the content. End-of-chapter practice exercises help to further reinforce important concepts.Each chapter also includes homework exercises and additional suggested readings on core topics. The text provides several example programs written in C and Java for use in several programming environments and a Linux Virtual Machine that can be run on Windows 10, Linux, or macOS.

Preschoolers will build critical thinking skills and logic through problem-solving exercises, including Hidden Pictures® puzzles, mazes, and more, all in an affordable, travel-friendly pad format.It's

The Earth and environmental sciences are becoming progressively more quantitative due to the increased use of mathematical models and new data analysis techniques. This accessible introduction presents an overview of the mathematical methods essential for understanding Earth processes, providing an invaluable resource for students and early career researchers who may have missed (or forgotten) the mathematics they need to succeed as scientists. Topics build gently from basic methods such as calculus to more advanced techniques including linear algebra and differential equations. The practical applications of the mathematical methods to a variety of topics are discussed, ranging from atmospheric science and oceanography to biogeochemistry and geophysics. Including over 530 exercises and end-of-chapter problems, as well as additional computer codes in Python and MATLAB®, this book supports readers in applying appropriate analytical or computational methods to solving real research questi

A state-of-the-art review of key topics in medical image perception science and practice, including associated techniques, illustrations and examples. This second edition contains extensive updates and substantial new content. Written by key figures in the field, it covers a wide range of topics including signal detection, image interpretation and advanced image analysis (e.g. deep learning) techniques for interpretive and computational perception. It provides an overview of the key techniques of medical image perception and observer performance research, and includes examples and applications across clinical disciplines including radiology, pathology and oncology. A final chapter discusses the future prospects of medical image perception and assesses upcoming challenges and possibilities, enabling readers to identify new areas for research. Written for both newcomers to the field and experienced researchers and clinicians, this book provides a comprehensive reference for those interes

An essential part of studying to become a physical scientist or engineer is learning how to solve problems. This book contains over 200 appropriate physics problems with hints and full solutions. The author demonstrates how to break down a problem into its essential components, and how to chart a course through them to a solution. With problem-solving skills being essential for any physical scientist or engineer, this book will be invaluable to potential and current undergraduates seeking a career in these fields. The book is divided into three parts: questions, hints and solutions. The questions section is subdivided into 15 chapters, each centred on a different area of physics, from elementary particles, through classical physics, to cosmology. The second section provides brief hints, whilst the third sets out full and explicit solutions to each problem. Most begin with thoughts that students might have after reading a problem, allowing the reader to understand which questions they s

An essential part of studying to become a physical scientist or engineer is learning how to solve problems. This book contains over 200 appropriate physics problems with hints and full solutions. The author demonstrates how to break down a problem into its essential components, and how to chart a course through them to a solution. With problem-solving skills being essential for any physical scientist or engineer, this book will be invaluable to potential and current undergraduates seeking a career in these fields. The book is divided into three parts: questions, hints and solutions. The questions section is subdivided into 15 chapters, each centred on a different area of physics, from elementary particles, through classical physics, to cosmology. The second section provides brief hints, whilst the third sets out full and explicit solutions to each problem. Most begin with thoughts that students might have after reading a problem, allowing the reader to understand which questions they s

The third edition of Reinforced and Prestressed Concrete continues to be the most comprehensive text for engineering students, instructors and practising engineers. Theoretical and practical aspects of analysis and design are presented in a clear, easy-to-follow manner and are complemented by numerous illustrative and design examples to aid students' comprehension of complex concepts. This edition has been fully updated to reflect recent amendments and addenda to the Australian Standard for Concrete Structures AS3600–2009 and allied standards. Two new chapters, covering T-beams, irregular-shaped sections and continuous beams, and strut-and-tie modelling have been added as discrete modules to enhance the progression of topics. Additional information is provided on fire resistance, detailing and covering, long-term deflection and design for torsion. An expanded collection of end-of-chapter tutorial problems consolidate student learning and develop problem-solving skills. Reinforced and P

Tough Test Questions? Missed Lectures? Not Enough Time? Textbook too pricey?Fortunately, there's Schaum's. This all-in-one-package includes more than 400 practice exercises to sharpen your problem-solving skills. Plus, you will have access to the revised online Schaum’s.com website—it's just like having your own virtual tutor! You'll find everything you need to build your confidence, skills, and knowledge and achieve highest score possible.Updated to address the latest course scope and sequence, Schaum’s Outline of German Grammar, Sixth Edition provides a systematic review of the language with clear and concise explanations of key grammar concepts. Packed with hands-on exercise, the guide allows you to work on your own—for both initial learning and for review. You’ll also get access to a variety of audio recordings online via the new and improved Schaums.com website. Reflecting the most modern usages and topics in German language study, this guide will help you develop a better underst