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.

Epidemiology Kept Simple - An Introduction To Traditional And Modern Epidemiology 3E (3版)

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【簡介】 Epidemiology Kept Simple introduces the epidemiological principles and methods that are increasingly important in the practice of medicine and public health. With minimum use of technical language it fully explains terminology, concepts, and techniques associated with traditional and modern epidemiology. Topics include disease causality, epidemiologic measures, descriptive epidemiology, study design, clinical and primary prevention trials, observational cohort studies, case-control studies, and the consideration of random and systematic error in studies of causal factors. Chapters on the infectious disease process, outbreak investigation, and screening for disease are also included. The latter chapters introduce more advanced biostatistical and epidemiologic techniques, such as survival analysis, Mantel-Haenszel techniques, and tests for interaction.This third edition addresses all the requirements of the American Schools of Public Health (ASPH) Epidemiological Competencies, and provides enhanced clarity and readability on this difficult subject. Updated with new practical exercises, case studies and real world examples, this title helps you develop the necessary tools to interpret epidemiological data and prepare for board exams, and now also includes review questions at the end of each chapter.Epidemiology Kept Simple continues to provide an introductory guide to the use of epidemiological methods for graduate and undergraduate students studying public health, health education and nursing, and for all practicing health professionals seeking professional development.