{"product_id":"optimization-for-learning-and-control-anders-hansson-9781119809135","title":"Optimization for Learning and Control","description":"\u003cb\u003eOptimization for Learning and Control\u003c\/b\u003e \u003cp\u003e\u003cb\u003eComprehensive resource providing a masters' level introduction to optimization theory and algorithms for learning and control\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eOptimization for Learning and Control\u003c\/i\u003e describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems. \u003c\/p\u003e\u003cp\u003eSeveral applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. \u003c\/p\u003e\u003cp\u003eToday, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters' level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters' students in a coherent way. The focus is on basic algorithmic principles and trade-offs. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eOptimization for Learning and Control\u003c\/i\u003e covers sample topics such as: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eOptimization theory and optimization methods, covering classes of optimization problems like least squares problems, quadratic problems, conic optimization problems and rank optimization.\u003c\/li\u003e \u003cli\u003eFirst-order methods, second-order methods, variable metric methods, and methods for nonlinear least squares problems.\u003c\/li\u003e \u003cli\u003eStochastic optimization methods, augmented Lagrangian methods, interior-point methods, and conic optimization methods.\u003c\/li\u003e \u003cli\u003eDynamic programming for solving optimal control problems and its generalization to reinforcement learning.\u003c\/li\u003e \u003cli\u003eHow optimization theory is used to develop theory and tools of statistics and learning, e.g., the maximum likelihood method, expectation maximization, k-means clustering, and support vector machines.\u003c\/li\u003e \u003cli\u003eHow calculus of variations is used in optimal control and for deriving the family of exponential distributions.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eOptimization for Learning and Control\u003c\/i\u003e is an ideal resource on the subject for scientists and engineers learning about which optimization methods are useful for learning and control problems; the text will also appeal to industry professionals using machine learning for different practical applications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Anders Hansson, Martin Andersen\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 1119809134\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9781119809135\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Wiley\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 06\/07\/2023\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 432\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 2.10lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.94d\u003c\/p\u003e","brand":"Anders Hansson","offers":[{"title":"Hardcover","offer_id":44966255460607,"sku":"9781119809135","price":130.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_8dbe7d8c-0807-422b-b63d-c340f98f6b78.jpg?v=1711159323","url":"https:\/\/www.whiterainbookhouse.com\/products\/optimization-for-learning-and-control-anders-hansson-9781119809135","provider":"WR Book House","version":"1.0","type":"link"}