Modeling Change and Uncertainty Machine Learning and Other Techniques
Modeling Change and Uncertainty: Machine Learning and Other Techniques by William P. Fox is a comprehensive resource published by C&H/CRC Press on August 26, 2024. This 446-page book is written in English and focuses on the craft of mathematical modeling, emphasizing the importance of practice in developing problem-solving skills. The authors aim to foster lifelong learning and cultivate competent decision-makers for the 21st century through a structured approach to mathematical modeling.
Readers will find a problem-solving methodology that introduces various mathematical modeling topics through practical problems. The book covers both linear and nonlinear models of discrete dynamical systems, statistics, and probability modeling, establishing a solid foundation for handling data. It also discusses the use of ordinary differential equations, linear programming, and machine learning to support decision-making processes. Additionally, the text addresses the realities of uncertainty and randomness, providing valuable techniques applicable in business and financial contexts, and includes practical tools such as Excel, MAPLE, and R for modeling applications.
Official synopsis Publisher
“Mathematical modeling is a powerful craft that requires practice. The more practice the better one will become in executing the art. The authors wrote this book to develop the craft of mathematical modeling and to foster a desire for lifelong learning, habits of mind and develop competent and confident problem solvers and decision makers for the 21st century. This book offers a problem-solving approach. The authors introduce a problem to help motivate the learning of a particular mathematical modeling topic. The problem provides the issue or what is needed to solve using an appropriate modeling technique. Then principles are applied to the problem and present the steps in obtaining an appropriate model to solve the problem. Modeling Change and Uncertainty: Covers both linear and nonlinear models of discrete dynamical systems. Introduces statistics and probability modeling Introduces critical statistical concepts to handle univariate and multivariate data. stablishes a foundation in probability modeling. Uses ordinary differential equations (ODEs) to develop a more robust solution to problems. Uses linear programming and machine learning to support decision making. Introduces the reality of uncertainty and randomness that is all around us Discusses the use of linear programing to solve common problems in modern industry. Discusses he power and limitations of simulations Introduces the methods and formulas used in businesses and financial organizations. Introduces valuable techniques using Excel, MAPLE, and R. Mathematical modeling offers a framework for decision makers in all fields. This framework consists of four key components: the formulation process, the solution process, interpretation of the solution in the context of the actual problem, and sensitivity analysis”–
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