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The new edition of this best-selling text stresses an understanding of mathematical concepts relevant to the machine trades/manufacturing and overcomes the often mechanical "plug in" approach found in many trade-related texts. A complete grasp of those concepts is emphasized in the presentation and application of all topics from general math to oblique trigonometry, compound angles, and numerical control. The presentations are accompanied by realistic industry-related examples, illustrations, and actual applications, which progress from the simple to the relatively complex. The analytic approach that is necessary in translating engineering drawing dimensions to machine working dimensions is emphasized throughout. Integration of algebraic and geometric principles with trigonometry by careful sequence and treatment of material also helps the student in solving industrial applications.
Strengthen mathematical skills and gain practice using those skills in preparation for today's machine trades or manufacturing with Peterson/Smith's MATHEMATICS FOR MACHINE TECHNOLOGY, 8E. This comprehensive book connects math concepts to relevant machine applications, using industry-specific examples, realistic illustrations and actual machine functions. Step-by-step problems and examples progress from general math to more complex trigonometry and solid geometry while demonstrating how math applies to machine trades and manufacturing fields. The authors highlight calculator operations, when appropriate, while new coverage emphasizes spreadsheets and introductory G- and M- codes for CNC programming. Master the practical, vocational and technical applications of math concepts necessary to excel in today's machine, tool-and-die and tool design industries with this proven book. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.