This resource is written for numeracy learners working in steel, aluminum and other metals / plastics manufacturing roles. It is specifically targeted towards machinists / machine operators and covers realistic math problems that manufacturers encounter in the workplace. The resource begins with basic operators and moves onto more complex equations. Table of contents: * Whole numbers. * Common fractions. * Decimal fractions. * Direct measure. * Computed measure. * Percent and finance. * Graphs. * Shop formulas. * Ration and proportion. * Powers and roots. * Geometric forms and construction. * Trigonometry. * Appendix. Glossary. Odd numbered answers.

This book covers a variety of topics in mathematics as they relate to industrial technologies including manufacturing, electricity/electronics, graphics, communication, transportation, industrial management, materials and related science principles. Organized by topics, the main objective is to develop strong, logical problem-solving skills. ..A brief description of each math principle is presented with step-by-step examples. The explanations are designed to emphasize the correct use and application of math principles. Graphs, drawings and charts relating to the applications reinforce the use of the skills developed. ALSO AVAILABLE INSTRUCTOR SUPPLEMENTS CALL CUSTOMER SUPPORT TO ORDER Instructor's Guide, ISBN: 0-8273-6975-1

Students learn how to solve problems they'll encounter in their professional lives with this concise single-volume treatment. It employs MATLAB and other strategies to explore typical industrial problems. 2000 edition.

Advances in Mathematics for Industry 4.0 examines key tools, techniques, strategies, and methods in engineering applications. By covering the latest knowledge in technology for engineering design and manufacture, chapters provide systematic and comprehensive coverage of key drivers in rapid economic development. Written by leading industry experts, chapter authors explore managing big data in processing information and helping in decision-making, including mathematical and optimization techniques for dealing with large amounts of data in short periods. - Focuses on recent research in mathematics applications for Industry 4.0 - Provides insights on international and transnational scales - Identifies mathematics knowledge gaps for Industry 4.0 - Describes fruitful areas for further research in industrial mathematics, including forthcoming international studies and research

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.