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This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
This comprehensive guide to sustainable energy builds robust connections between abstract theory and practical applications, providing students with a global perspective on this most timely subject. Includes a succinct refresher on essential thermodynamics, fluid mechanics and heat transfer, giving students a solid foundation on which to build. Introduces technologies for hydropower, biomass, geothermal, ocean, solar and wind energy, and fuel cells, with material on nuclear energy, fossil fuel generation and future energy directions, providing a consistent framework for analyzing past, present and future energy systems. Provides coding examples, and real-world case studies, giving students experience in applying theory to practice. Supported by topics for classroom debate, video solutions, and links to online resources, to interactively engage students and inspire further exploration. With a consistent structure and pedagogy, over 160 end-of-chapter problems, and solutions for instructors, this is the ideal introduction for senior undergraduate and graduate students, and a resource for energy professionals.
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Wilfried Brutsaert (2022 Stockholm Water Prize Laureate) has revised and updated his classic textbook to take into account recent developments, while retaining the rigor and structure of the previous edition to introduce the fundamental principles of hydrology. New topics include the response of the global water cycle to climate change, the land surface energy budget closure, snow melt, groundwater trends and statistical surface variability with disturbed atmospheric boundary layers. Hydrologic phenomena are dealt with at the spatial and temporal scales at which they occur in nature. The physics and mathematics necessary to describe these phenomena are introduced and developed: readers will require a working knowledge of calculus and basic fluid mechanics. This classroom-tested textbook – based on the author's long-running course at Cornell - is invaluable for entry-level courses in hydrology directed at advanced undergraduate and graduate students in physical science and engineering. In addition, it is also a great reference text for practising scientists and engineers.
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Presenting students with a comprehensive and efficient approach to the modelling, simulation, and analysis of dynamic systems, this textbook addresses mechanical, electrical, thermal and fluid systems, feedback control systems, and their combinations. It features a robust introduction to fundamental mathematical prerequisites, suitable for students from a range of backgrounds; clearly established three-key procedures – fundamental principles, basic elements, and ways of analysis – for students to build on in confidence as they explore new topics; over 300 end-of-chapter problems, with solutions available for instructors, to solidify a hands-on understanding; and clear and uncomplicated examples using MATLAB®/Simulink® and Mathematica®, to introduce students to computational approaches. With a capstone chapter focused on the application of these techniques to real-world engineering problems, this is an ideal resource for a single-semester course in dynamic systems for students in mechanical, aerospace and civil engineering.
Acquire complete knowledge of the basics of air-breathing turbomachinery with this hands-on practical text. This updated new edition for students in mechanical and aerospace engineering discusses the role of entropy in assessing machine performance, provides a review of flow structures, and includes an applied review of boundary layer principles. New coverage describes approaches used to smooth initial design geometry into a continuous flow path, the development of design methods associated with the flow over blade shape (cascades loss theory) and annular type flows, as well as a discussion of the mechanisms for the setting of shaft speed. This essential text is also fully supported by over 200 figures, numerous examples, and homework problems, many of which have been revised for this edition.
Introduces the two most common numerical methods for heat transfer and fluid dynamics equations, using clear and accessible language. This unique approach covers all necessary mathematical preliminaries at the beginning of the book for the reader to sail smoothly through the chapters. Students will work step-by-step through the most common benchmark heat transfer and fluid dynamics problems, firmly grounding themselves in how the governing equations are discretized, how boundary conditions are imposed, and how the resulting algebraic equations are solved. Providing a detailed discussion of the discretization steps and time approximations, and clearly presenting concepts of explicit and implicit formulations, this graduate textbook has everything an instructor needs to prepare students for their exams and future careers. Each illustrative example shows students how to draw comparisons between the results obtained using the two numerical methods, and at the end of each chapter they can test and extend their understanding by working through the problems provided. A solutions manual is also available for instructors.
Analog and digital electronics are an important part of most modern courses in physics. Closely mapped to the current UGC CBCS syllabus, this comprehensive textbook will be a vital resource for undergraduate students of physics and electronics. The content is structured to emphasize fundamental concepts and applications of various circuits and instruments. A wide range of topics like semiconductor physics, diodes, transistors, amplifiers, Boolean algebra, combinational and sequential logic circuits, and microprocessors are covered in lucid language and illustrated with many diagrams and examples for easy understanding. A diverse set of questions in each chapter, including multiple-choice, reasoning, numerical, and practice problems, will help students consolidate the knowledge gained. Finally, computer simulations and project ideas for projects will help readers apply the theoretical concepts and encourage experiential learning.
This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.
The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
Offering a new perspective, this textbook demystifies the operation of electric machines by providing an integrated understanding of electromagnetic fields, electric circuits, numerical analysis, and computer programming. It presents fundamental concepts in a rigorous manner, emphasising underlying physical modelling assumptions and limitations, and provides detailed explanations of how to implement the finite element method to explore these concepts using Python. It includes explanations of the conversion of concepts into algorithms, and algorithms into code, and examples building in complexity, from simple linear-motion electromagnets to rotating machines. Over 100 theoretical and computational end-of-chapter exercises test understanding, with solutions for instructors and downloadable Python code available online.Ideal for graduates and senior undergraduates studying electric machines, electric machine design and control, and power electronic converters and power systems engineering, this textbook is also a solid reference for engineers interested in understanding, analysing and designing electric motors, generators, and transformers.
This textbook provides a comprehensive treatment of irrigation engineering for advanced undergraduates and graduate students. It does not require a background in calculus, hydrology, or hydraulics, offering a one-stop overview of the entire field of study. It includes everything a student of irrigation engineering needs to know: concepts of climate, soils, crops, water quality, hydrology, and hydraulics, as well as their application to design and environmental management. To demonstrate the practical applications of the theories discussed, there are over 300 worked examples and end-of chapter exercises. The exercises allow readers to solve real-world problems and apply the information they've learned to a diverse range of scenarios. To further prepare students for their future careers, each chapter includes many illustrative diagrams and tables containing data to help design irrigation systems. For instructors' use when planning and teaching, a solutions manual can be found online alongside a suite of PowerPoint lecture slides.
In modern computer science, there exists no truly sequential computing system; and most advanced programming is parallel programming. This is particularly evident in modern application domains like scientific computation, data science, machine intelligence, etc. This lucid introductory textbook will be invaluable to students of computer science and technology, acting as a self-contained primer to parallel programming. It takes the reader from introduction to expertise, addressing a broad gamut of issues. It covers different parallel programming styles, describes parallel architecture, includes parallel programming frameworks and techniques, presents algorithmic and analysis techniques and discusses parallel design and performance issues. With its broad coverage, the book can be useful in a wide range of courses; and can also prove useful as a ready reckoner for professionals in the field.
Written for a one-semester course in hydraulics, this concise textbook is rooted in the fundamental principles of fluid mechanics and aims to promote sound hydraulic engineering practice. Basic methods are presented to underline the theory and engineering applications, and examples and problems build in complexity as students work their way through the textbook. Abundant worked examples and calculations, real-world case studies, and revision exercises, as well as precisely crafted end-of-chapter exercises ensure students learn exactly what they need in order to consolidate their knowledge and progress in their career.Students learn to solve pipe networks, optimize pumping systems, design pumps and turbines, solve differential equations for gradually-varied flow and unsteady flow, and gain knowledge of hydraulic structures like spillways, gates, valves, and culverts. An essential textbook for intermediate to advanced undergraduate and graduate students in civil and environmental engineering.
Providing a concise and accessible overview of the design, implementation and management of medical software, this textbook will equip students with a solid understanding of critical considerations for both standalone medical software (software as a medical device/SaMD) and software that is integrated into hardware devices. It includes: practical discussion of key regulatory documents and industry standards, and how these translate into concrete considerations for medical software design; detailed coverage of the medical software lifecycle process ; accessible introduction to quality and risk management systems in the context of medical software; succinct coverage of essential topics in data science, machine learning, statistics, cybersecurity, software engineering and healthcare bring readers up-to-speed; six cautionary real-world case studies illustrate the dangers of improper or careless software processes. Accompanied by online resources for instructors, this is the ideal introduction for undergraduate students in biomedical engineering, electrical engineering and computer science, junior software engineers, and digital health entrepreneurs.