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Introduction to Probability for Data Science

2021 Computer science and applied mathematics
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Release : 2021
Genre : Computer science and applied mathematics
Kind : eBook
Book Rating : 464/5 ( reviews)

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Book Synopsis Introduction to Probability for Data Science by : Stanley H. Chan

Download or read book Introduction to Probability for Data Science written by Stanley H. Chan. This book was released on 2021. Available in PDF, EPUB and Kindle. Book excerpt: "Probability is one of the most interesting subjects in electrical engineering and computer science. It bridges our favorite engineering principles to the practical reality, a world that is full of uncertainty. However, because probability is such a mature subject, the undergraduate textbooks alone might fill several rows of shelves in a library. When the literature is so rich, the challenge becomes how one can pierce through to the insight while diving into the details. For example, many of you have used a normal 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 traffic, but where does the gorgeous Poisson equation come from? This book is designed to fill these gaps with knowledge that is essential to all data science students." -- Preface.

Probability for Data Scientists (First Edition)

2019-05-31 Computer science
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Release : 2019-05-31
Genre : Computer science
Kind : eBook
Book Rating : 704/5 ( reviews)

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Book Synopsis Probability for Data Scientists (First Edition) by : Juana Sánchez

Download or read book Probability for Data Scientists (First Edition) written by Juana Sánchez. This book was released on 2019-05-31. Available in PDF, EPUB and Kindle. Book excerpt: Probability for Data Scientists provides students with a mathematically sound yet accessible introduction to the theory and applications of probability. Students learn how probability theory supports statistics, data science, and machine learning theory by enabling scientists to move beyond mere descriptions of data to inferences about specific populations. The book is divided into two parts. Part I introduces readers to fundamental definitions, theorems, and methods within the context of discrete sample spaces. It addresses the origin of the mathematical study of probability, main concepts in modern probability theory, univariate and bivariate discrete probability models, and the multinomial distribution. Part II builds upon the knowledge imparted in Part I to present students with corresponding ideas in the context of continuous sample spaces. It examines models for single and multiple continuous random variables and the application of probability theorems in statistics. Probability for Data Scientists effectively introduces students to key concepts in probability and demonstrates how a small set of methodologies can be applied to a plethora of contextually unrelated problems. It is well suited for courses in statistics, data science, machine learning theory, or any course with an emphasis in probability. Numerous exercises, some of which provide R software code to conduct experiments that illustrate the laws of probability, are provided in each chapter.

High-Dimensional Probability

2018-09-27 Business & Economics
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Release : 2018-09-27
Genre : Business & Economics
Kind : eBook
Book Rating : 199/5 ( reviews)

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Book Synopsis High-Dimensional Probability by : Roman Vershynin

Download or read book High-Dimensional Probability written by Roman Vershynin. This book was released on 2018-09-27. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Introduction to Probability

2017-11-02 Mathematics
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Release : 2017-11-02
Genre : Mathematics
Kind : eBook
Book Rating : 98X/5 ( reviews)

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Book Synopsis Introduction to Probability by : David F. Anderson

Download or read book Introduction to Probability written by David F. Anderson. This book was released on 2017-11-02. Available in PDF, EPUB and Kindle. Book excerpt: This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications. Introduction to Probability covers the material precisely, while avoiding excessive technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text offers the reader a first glimpse of the major theorems of the subject: the law of large numbers and the central limit theorem. The important probability distributions are introduced organically as they arise from applications. The discrete and continuous sides of probability are treated together to emphasize their similarities. Intended for students with a calculus background, the text teaches not only the nuts and bolts of probability theory and how to solve specific problems, but also why the methods of solution work.

Probability and Statistics for Data Science

2019-06-21 Business & Economics
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Release : 2019-06-21
Genre : Business & Economics
Kind : eBook
Book Rating : 117/5 ( reviews)

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Book Synopsis Probability and Statistics for Data Science by : Norman Matloff

Download or read book Probability and Statistics for Data Science written by Norman Matloff. This book was released on 2019-06-21. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.

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