ELEMENTS OF DISCRETE MATHEMATICS (2版)

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書名：Elements of Discrete Mathematics 2/e 作者：LIU 出版社：McGraw-Hill 出版日期：1985/00/00 ISBN：9780071005449 內容簡介 This book presents a selection of topics from set theory, combinatorics, graph theory, and algebra which were been considered as basic and useful to students in Applied Mathematics, Computer Seience, and Engineering. It's intended to be a textbook for a course in Discrete Mathematics at the sophomore-junior level, although it can also be used in a freshman-level course since the presentation does not assume any background beyond high-school mathematics. 目錄 1. Sets and Propositions 2. Computability and Formal Languages 3. Permutations, Combinations, and Discrete Probability 4. Relations and Functions 5. Graphs and Planar Graphs 6. Trees and Cut-Sets 7. Finite State Machines 8. Analysis of Algorithms 9. Discrete Numberic Functions and Generating Functions 10. Recurrence Relations and Recursive Algorithms 11. Groups and Rings 12. Boolean Algebras

Princeton Review AP Statistics Premium Prep (21版)

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【簡介】 PREMIUM PREP FOR A PERFECT 5! Ace the newly-digital AP Statistics Exam with this comprehensive study guide--including 5 full-length practice tests with answer explanations, timed online practice, and thorough content reviews.The Princeton Review ExpertiseStudy with prep and practice written entirely by AP educatorsLearn test-taking strategies backed by 40+ years of test prep success Focused, supportive lessons designed and perfected by expertsEverything You Need for a High ScoreA step-by-step guide on how to better your score with this bookComprehensive review of all topics on the new digital examCustomize a study plan and target areas of improvement by using our diagnostic answer keyEnd-of-chapter drills for each topic to reinforce learningExclusive online digital flashcards to hone essential conceptsPremium Practice for AP Excellence5 full-length practice tests (2 in the book, 3 online)Detailed answer explanations to help you learn from mistakesOnline tests provided as both digital versions (with timer option to simulate exam experience), and as downloadable PDFs (with interactive elements mimicking the exam interface)Get more via your online student tools--including a list of key terms and concepts, study plans, and exam updates

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

Statistics for Chemical Engineers: From Data to Models to Decisions (1版)

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【簡介】 Build a firm foundation for studying statistical modelling, data science, and machine learning with this practical introduction to statistics, written with chemical engineers in mind. It introduces a data-model-decision approach to applying statistical methods to real-world chemical engineering challenges, establishes links between statistics, probability, linear algebra, calculus, and optimization, and covers classical and modern topics such as uncertainty quantification, risk modelling, and decision-making under uncertainty. Over 100 worked examples using Matlab and Python demonstrate how to apply theory to practice, with over 70 end-of-chapter problems to reinforce student learning, and key topics are introduced using a modular structure, which supports learning at a range of paces and levels. Requiring only a basic understanding of calculus and linear algebra, this textbook is the ideal introduction for undergraduate students in chemical engineering, and a valuable preparatory text for advanced courses in data science and machine learning with chemical engineering applications.