Intro to probability for data science
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Intro to probability for data science
Introduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 S Intro to probability for data science Stanley H. ChanThis book is published by Michigan Publishing under an agreement with the author. It is made available free of charge in electronic form to any student or instructor interested in the subject matter.Published in the United Stales of America by Michigan PublishingManufactured in the Un Intro to probability for data science ited Stales of AmericaISBN 978-1 -60785-746-1 (hardcover)ISBN 978-1-60785-717-1 (electronic)iiTo Vivian. Joanna, and Cynthia ChanAnd ye shall know theIntro to probability for data science
truth, and the truth shall make you free.John 8:32iiiPrefaceThis book is an int roductory textbook in undergraduate probability. It has a mission: toIntroduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 S Intro to probability for data science g t he course, 1 have dist illed what 1 believe to be the core of probabilistic methods. 1 put t he book in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time.Probability is one of the most interesting subjects in electrical engi Intro to probability for data science neering and computer science. It bridges our favorite engineering principles to t he practical reality, a world that is full of uncertainty. However,Intro to probability for data science
because probability is such a mature subject, t he undergraduate textbooks alone might fill several rows of shelves in a library. When the literature Introduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 S Intro to probability for data science al random variable before, but have you ever wondered where the “bell shape" Comes from? Every probability class will teach you about flipping a coin, but how can “flipping a coin" ever be useful in machine learning today? Data scientists use the Poisson random variables to model the internet traffi Intro to probability for data science c, but where does the gorgeous Poisson equation come from? This book is designed to fill these gaps with knowledge t hat is essent ial to all data sciIntro to probability for data science
ence students.This leads to the three goals of the book, (i) Motivation: In the ocean of mathematical definitions, theorems, and equations, why shouldIntroduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 S Intro to probability for data science or physics beyond those equations? (iii) Implication: After we have learned a topic, what new problems can we solve?The hook's intended audience is undergraduate juniors/seniors and first-year graduate students majoring in electrical engineering and computer science. The prerequisites are standard u Intro to probability for data science ndergraduate linear algebra and calculus, except for the section about characteristic functions, where Fourier transforms are needed. An undergraduateIntro to probability for data science
course in signals and systems would suffice, even taken concurrently while studying this book.The length of the book is suit able for a t wo-semesterIntroduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 S Intro to probability for data science 1-5 as its backbone. Chapter 6 on sample statistics is suitable for students who wish to gain theoretical insights into probabilistic convergence. Chapter 7 on regression and Chapter 8 on estimation best suit students who want to pursue machine learning and signal processing. Chapter 9 discusses con Intro to probability for data science fidence int ervals and hypothesis testing, which are critical to modern dat a analysis. Chapter 10 introduces random processes. My approach for randomIntro to probability for data science
processes is more tailored to informat ion processing and communication systems, which are usually more relevant to electrical engineering students.AIntroduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 S Intro to probability for data science ee many “practice exorcises”, which arc easy problems with worked-out solutions. They can be skipped without loss to the flow of the book.Acknowledgements: If 1 could thank only one person, it must be Professor I’awwaz Ulaby of the University of Michigan. Professor Ulaby has been the source of suppo Intro to probability for data science rt in all aspects, from the book’s layout to technical content, proofreading, and marketing. The book would not have been published without the help oIntro to probability for data science
f Professor Ulaby. 1 am deeply moved by Professor Ulaby's vision that education should be made accessible to all students. With textbook prices rocketIntroduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 S Intro to probability for data science Thank you. lawwaz, for your unbounded support technically, mentally, and financially. Thank you also for recommending Richard Carnes, rhe meticulous details Richard offered have significantly improved the fluency of the book. Thank you. Richard.1 thank my colleagues at Purdue who had shared many th Intro to probability for data science oughts with me when I taught the course (in alphabetical order): Professors Mark Roll, Mary Comer, Saul Gelfand, Amy Reibnian, and Chih-Chnn Wang. MyIntro to probability for data science
teaching assistant I-Fan Lin was instrumental in the early development of this book. To the graduate students of my lab (Yihong Chi, Nick Chimitt, KenIntroduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 S Intro to probability for data science nish the book without your participation. A few students I taught volunteered to help edit the book: Benjamin Gottfried. Harrison Ilsueh. Dawoon Jung. Antonio Kincaid. Deepak Ravikumar. Krister Ulvog. Peace Umoru, Zhijing Yao. 1 would like to thank my Ph.D. advisor Professor Truong Nguyen for encour Intro to probability for data science aging me to write the book.Finally, 1 would like to thank my wife Vivian and my daughters, Joanna and Cynthia, for their love, patience, and support.Introduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 SIntroduction toPROBABILITY forDATA SCIENCEonStanley H. ChanIntroduction to ProbabilityforData ScienceStanley H. ChanPurdue UniversityCopyright ©2021 SGọi ngay
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