# Statistics with Julia

## Fundamentals for Data Science, Machine Learning and Artificial Intelligence

by Hayden Klok, Yoni Nazarathy  ### Book Description

Ccurrently many of Julia's users are hard-core developers that contribute to the language's standard libraries, and to the extensive package eco-system that surrounds it. Therefore, much of the Julia material available at present is aimed at other developers rather than end users. This is where our book comes in, as it has been written with the end-user in mind. The code examples have been deliberately written in a simple format, sometimes at the expense of efficiency and generality, but with the advantage of being easily readable. Each of the code examples aims to convey a specific statistical point, while covering Julia programming concepts in parallel. In a way, the code examples are reminiscent of examples that a lecturer may use in a lecture to illustrate concepts. The content of the book is written in a manner that does not assume any prior statistical knowledge, and in fact only assumes some basic programming experience and a basic understanding of mathematical notation.

Chapter 1
Introducing Julia

Chapter 2
Basic Probability

Chapter 3
Probability Distributions

Chapter 4
Processing and Summarizing Data

Chapter 5
Statistical Inference Ideas

Chapter 6
Confidence Intervals

Chapter 7
Hypothesis Testing

Chapter 8
Linear Regression

Chapter 9
Machine Learning Basics

Chapter 10
Simulation of Dynamic Models

Appendix A
How-to in Julia

Appendix B

Appendix C

Subject
Computer Science
Publisher
Self-publishing
Published
2020
Pages
413
Edition
1
Language
English
PDF Size
13.3 MB

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