A Primer on Reproducing Kernel Hilbert Spaces - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Primer on Reproducing Kernel Hilbert Spaces write by Jonathan H. Manton. This book was released on 2015-11-20. A Primer on Reproducing Kernel Hilbert Spaces available in PDF, EPUB and Kindle. Hilbert space theory is an invaluable mathematical tool in numerous signal processing and systems theory applications. Hilbert spaces satisfying certain additional properties are known as Reproducing Kernel Hilbert Spaces (RKHSs). This primer gives a gentle and novel introduction to RKHS theory. It also presents several classical applications. It concludes by focusing on recent developments in the machine learning literature concerning embeddings of random variables. Parenthetical remarks are used to provide greater technical detail, which some readers may welcome, but they may be ignored without compromising the cohesion of the primer. Proofs are there for those wishing to gain experience at working with RKHSs; simple proofs are preferred to short, clever, but otherwise uninformative proofs. Italicised comments appearing in proofs provide intuition or orientation or both. A Primer on Reproducing Kernel Hilbert Spaces empowers readers to recognize when and how RKHS theory can profit them in their own work.
A Primer on Machine Learning Applications in Civil Engineering
A Primer on Machine Learning Applications in Civil Engineering - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Primer on Machine Learning Applications in Civil Engineering write by Paresh Chandra Deka. This book was released on 2019-10-28. A Primer on Machine Learning Applications in Civil Engineering available in PDF, EPUB and Kindle. Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB® exercises
Mathematics for Machine Learning
Mathematics for Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mathematics for Machine Learning write by Marc Peter Deisenroth. This book was released on 2020-04-23. Mathematics for Machine Learning available in PDF, EPUB and Kindle. The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Kernel Methods in Computational Biology
Kernel Methods in Computational Biology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Kernel Methods in Computational Biology write by Bernhard Schölkopf. This book was released on 2004. Kernel Methods in Computational Biology available in PDF, EPUB and Kindle. A detailed overview of current research in kernel methods and their application to computational biology.
An Introduction to the Mathematical Theory of Inverse Problems
An Introduction to the Mathematical Theory of Inverse Problems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook An Introduction to the Mathematical Theory of Inverse Problems write by Andreas Kirsch. This book was released on 2021-02-15. An Introduction to the Mathematical Theory of Inverse Problems available in PDF, EPUB and Kindle. This graduate-level textbook introduces the reader to the area of inverse problems, vital to many fields including geophysical exploration, system identification, nondestructive testing, and ultrasonic tomography. It aims to expose the basic notions and difficulties encountered with ill-posed problems, analyzing basic properties of regularization methods for ill-posed problems via several simple analytical and numerical examples. The book also presents three special nonlinear inverse problems in detail: the inverse spectral problem, the inverse problem of electrical impedance tomography (EIT), and the inverse scattering problem. The corresponding direct problems are studied with respect to existence, uniqueness, and continuous dependence on parameters. Ultimately, the text discusses theoretical results as well as numerical procedures for the inverse problems, including many exercises and illustrations to complement coursework in mathematics and engineering. This updated text includes a new chapter on the theory of nonlinear inverse problems in response to the field’s growing popularity, as well as a new section on the interior transmission eigenvalue problem which complements the Sturm-Liouville problem and which has received great attention since the previous edition was published.
