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Cover: 9783030709006 | Statistics with Julia | Hayden Klok (u. a.) | Buch | xii | Englisch
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Statistics with Julia
Fundamentals for Data Science, Machine Learning and Artificial Intelligence
Buch von Hayden Klok (u. a.)
Sprache: Englisch

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Beschreibung
This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics.
The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book¿s associated GitHub repository online.
See what co-creators of the Julia language are saying about the book:
Professor Alan Edelman, MIT: With ¿Statistics with Juliä, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference.
Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language.This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.
This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics.
The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book¿s associated GitHub repository online.
See what co-creators of the Julia language are saying about the book:
Professor Alan Edelman, MIT: With ¿Statistics with Juliä, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference.
Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language.This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.
Zusammenfassung

Includes over 200 short code examples to illustrate dozens of key statistics concepts

Solidifies the understanding of probability and statistics of professionals that are already working in data science, machine learning, or artificial intelligence

Focuses on concepts to improve fundamental understanding

Inhaltsverzeichnis
Introducing Julia.- Basic Probability.- Probability Distributions.- Processing and Summarizing Data.- Statistical Inference Concepts.- Confidence Intervals.- Hypothesis Testing.- Linear Regression and Extensions.- Machine Learning Basics.- Simulation of Dynamic Models.- Appendix A: How-to in Julia.- Appendix B: Additional Julia Features.- Appendix C: Additional Packages.
Details
Erscheinungsjahr: 2021
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Springer Series in the Data Sciences
Inhalt: xii
527 S.
18 s/w Illustr.
130 farbige Illustr.
527 p. 148 illus.
130 illus. in color.
ISBN-13: 9783030709006
ISBN-10: 3030709000
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Klok, Hayden
Nazarathy, Yoni
Auflage: 1st ed. 2021
Hersteller: Springer International Publishing
Springer International Publishing AG
Springer Series in the Data Sciences
Maße: 285 x 215 x 35 mm
Von/Mit: Hayden Klok (u. a.)
Erscheinungsdatum: 04.09.2021
Gewicht: 1,554 kg
Artikel-ID: 119621951
Zusammenfassung

Includes over 200 short code examples to illustrate dozens of key statistics concepts

Solidifies the understanding of probability and statistics of professionals that are already working in data science, machine learning, or artificial intelligence

Focuses on concepts to improve fundamental understanding

Inhaltsverzeichnis
Introducing Julia.- Basic Probability.- Probability Distributions.- Processing and Summarizing Data.- Statistical Inference Concepts.- Confidence Intervals.- Hypothesis Testing.- Linear Regression and Extensions.- Machine Learning Basics.- Simulation of Dynamic Models.- Appendix A: How-to in Julia.- Appendix B: Additional Julia Features.- Appendix C: Additional Packages.
Details
Erscheinungsjahr: 2021
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Springer Series in the Data Sciences
Inhalt: xii
527 S.
18 s/w Illustr.
130 farbige Illustr.
527 p. 148 illus.
130 illus. in color.
ISBN-13: 9783030709006
ISBN-10: 3030709000
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Klok, Hayden
Nazarathy, Yoni
Auflage: 1st ed. 2021
Hersteller: Springer International Publishing
Springer International Publishing AG
Springer Series in the Data Sciences
Maße: 285 x 215 x 35 mm
Von/Mit: Hayden Klok (u. a.)
Erscheinungsdatum: 04.09.2021
Gewicht: 1,554 kg
Artikel-ID: 119621951
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