PROBABILISTIC MACHINE LEARNING: AN INTRODUCTION (1版)

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Probabilistic Machine Learning 系列名：Adaptive Computation and Machine Learning series ISBN13：9780262046824 出版社：Mit Pr 作者：Kevin P. Murphy 裝訂／頁數：精裝／864頁 出版日：2022/02/01

Introduction to Artificial Intelligence and Machine Learning (1版)

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【簡介】 Helps students unlock the power of AI and Machine Learning to achieve business success and future-proof their careersArtificial intelligence and machine learning are transforming the modern workplace, making AI literacy a critical skill for business professionals. Introduction to Artificial Intelligence and Machine Learning equips students with essential AI/ML knowledge and practical skills, enabling them to leverage cutting-edge technology in today’s data-driven world. With an engaging and accessible approach, this textbook ensures that students--regardless of technical background--gain a working knowledge of AI/ML systems. Concise, easy-to-digest chapters blend foundational concepts with real-world applications to help students develop the expertise needed to implement AI/ML solutions across industries. For instructors, the textbook offers flexible teaching methodologies, whether focusing on conceptual discussions, light technology applications, or full AI/ML projects. With a clear business perspective and a strong emphasis on AI governance and deployment, the textbook prepares students to navigate the future of AI in the workplace with confidence. Helping students build a solid foundation in key concepts while exploring strategic implementation and ethical considerations, Introduction to Artificial Intelligence and Machine Learning is ideal for undergraduate and graduate students in business, engineering, and healthcare programs taking courses such as Business Analytics, Information Systems, and AI Strategy. AN INTERACTIVE, MULTIMEDIA LEARNING EXPERIENCEThis textbook includes access to an interactive, multimedia e-text. Icons throughout the print book signal corresponding digital content in the e-text. Video Clips created by the author complement the text and engage students more deeply with AI/ML concepts and applications.Interactive Figures and Charts are integrated throughout the enhanced e-text to provide engaging visual representations of the material.Interactive Questions appear in each chapter of the enhanced e-text, providing students with immediate feedback to strengthen learning.

An Introduction to Optimization: With Applications to Machine Learning (5版)

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An Introduction to Optimization Accessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB® exercises and worked examples Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization, and applications of optimization in machine learning. Numerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB® exercises and practice problems that reinforce the discussed theory and algorithms. The Fifth Edition features a new chapter on Lagrangian (nonlinear) duality, expanded coverage on matrix games, projected gradient algorithms, machine learning, and numerous new exercises at the end of each chapter. An Introduction to Optimization includes information on: The mathematical definitions, notations, and relations from linear algebra, geometry, and calculus used in optimization Optimization algorithms, covering one-dimensional search, randomized search, and gradient, Newton, conjugate direction, and quasi-Newton methods Linear programming methods, covering the simplex algorithm, interior point methods, and duality Nonlinear constrained optimization, covering theory and algorithms, convex optimization, and Lagrangian duality Applications of optimization in machine learning, including neural network training, classification, stochastic gradient descent, linear regression, logistic regression, support vector machines, and clustering. An Introduction to Optimization is an ideal textbook for a one- or two-semester senior undergraduate or beginning graduate course in optimization theory and methods. The text is also of value for researchers and professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.