Statistics with Julia : fundamentals for data science, machine learning and artificial intelligence / Yoni Nazarathy, Hayden Klok.
2021
QA273.19.E4 N39 2021
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Online Access
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Unlimited
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Can lend chapters, not whole ebooks
Details
Title
Statistics with Julia : fundamentals for data science, machine learning and artificial intelligence / Yoni Nazarathy, Hayden Klok.
Author
Nazarathy, Yoni, author.
ISBN
9783030709013 (electronic bk.)
3030709019 (electronic bk.)
9783030709006
3030709000
3030709019 (electronic bk.)
9783030709006
3030709000
Published
Singapore : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource : illustrations (chiefly color)
Other Standard Identifiers
10.1007/978-3-030-70901-3 doi
Call Number
QA273.19.E4 N39 2021
Dewey Decimal Classification
519.2
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed September 16, 2021).
Added Author
Klok, Hayden, author.
Series
Springer series in the data sciences. 2365-5682
Available in Other Form
Print version: 9783030709006
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Online Access
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Table of Contents
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.
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.