Fundamentals of Probability (5版)

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【簡介】 This one- or two-term calculus-based basic probability text is written for majors in mathematics, physical sciences, engineering, statistics, actuarial science, business and finance, operations research, and computer science. It presents probability in a natural way: through interesting and instructive examples and exercises that motivate the theory, definitions, theorems, and methodology. This book is mathematically rigorous and, at the same time, closely matches the historical development of probability. Whenever appropriate, historical remarks are included, and the 2096 examples and exercises have been carefully designed to arouse curiosity and hence encourage students to delve into the theory with enthusiasm. 【目錄】 1. Axioms of Probability 2. Combinatorial Methods 3. Conditional Probability and Independence 4. Distribution Functions and Discrete Random Variables 5. Continuous Random Variables 6. Special Discrete Distributions 7. Special Continuous Distributions 8. Understanding Relationships: Covariance, Correlations, and Conditional Distributions 9. More Bivariate and Multivariate Topics 10. Inequalities and Limit Theorems Appendix Tables

Fundamentals of Probability and Statistics for Machine Learning (1版)

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【簡介】 An introductory textbook for undergraduate or beginning graduate students that integrates probability and statistics with their applications in machine learning.Most curricula have students take an undergraduate course on probability and statistics before turning to machine learning. In this innovative textbook, Ethem Alpaydın offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydın accessibly connects machine learning to its roots in probability and statistics, starting with the basics of random experiments and probabilities and eventually moving to complex topics such as artificial neural networks. With a practical emphasis and learn-by-doing approach, this unique text offers comprehensive coverage of the elements fundamental to an empirical understanding of machine learning in a data science context. Consolidates foundational knowledge and key techniques needed for modern data scienceCovers mathematical fundamentals of probability and statistics and ML basicsEmphasizes hands-on learningSuits undergraduates as well as self-learners with basic programming experienceIncludes slides, solutions, and code

Fundamentals of Thermodynamics (11版)

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目錄 1 Introduction and Preliminaries 2 Properties of a Pure Substance 3 Energy Equation and First Law of Thermodynamics 4 Energy Analysis for a Control Volume 5 The Second Law of Thermodynamics 6 Entropy 7 Entropy Analysis for a Control Volume 8 Exergy 9 Power and Refrigeration Systems—With Phase Change 10 Power and Refrigeration Systems—Gaseous Working Fluids Appendix A SI Units: Single-State Properties Appendix B SI Units: Thermodynamic Tables Appendix C Ideal Gas Specific Heat Appendix D Equations of State Appendix E Figures Appendix F English Unit Tables

Fundamentals of Transport Phenomena (1版)

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【簡介】 Fluid mechanics, solid state diffusion and heat conduction are deeply interconnected through the mathematics and physical principles that define them. This concise and authoritative book reveals these connections, providing a detailed picture of their important applications in astrophysics, plasmas, energy systems, aeronautics, chemical engineering and materials science. This sophisticated and focused text offers an alternative to more expansive volumes on heat, mass and momentum transfer and is ideal for students and researchers working on fluid dynamics, mass transfer or phase transformations and industrial scientists seeking a rigorous understanding of chemical or materials processes. Accessible yet in depth, this modern treatment distills the essential theory and application of these closely related topics, includes numerous real world applications and can be used for teaching a range of related courses in physics, engineering and materials science departments.